For further information, consult Google Scholar or PubMed.
2022 |
Kathryn M Parsley; Bernie J Daigle Jr.; Jaime L Sabel Initial Development and Validation of the Plant Awareness Disparity Index Journal Article CBE—Life Sciences Education, 21 (4), pp. ar64, 2022, (Publisher: American Society for Cell Biology (lse)). @article{parsley_initial_2022, title = {Initial Development and Validation of the Plant Awareness Disparity Index}, author = {Kathryn M Parsley and Bernie J Daigle Jr. and Jaime L Sabel}, url = {https://www.lifescied.org/doi/10.1187/cbe.20-12-0275}, doi = {10.1187/cbe.20-12-0275}, year = {2022}, date = {2022-12-01}, urldate = {2022-09-20}, journal = {CBE—Life Sciences Education}, volume = {21}, number = {4}, pages = {ar64}, abstract = {Plant awareness disparity (PAD, formerly plant blindness) is the idea that students tend not to notice or appreciate the plants in their environment. This phenomenon often leads to naïve points of view, such as plants are not important or do not do anything for humans. There are four components of PAD: attitude (not liking plants), attention (not noticing plants), knowledge (not understanding the importance of plants), and relative interest (finding animals more interesting than plants). Many interventions have been suggested to prevent PAD, but without an instrument shown to demonstrate valid inferences to measure PAD, it is difficult to tell whether these interventions are successful or not. We have developed and validated the Plant Awareness Disparity Index (PAD-I) to measure PAD and its four components in undergraduate biology students. The study population was 74.32% female and 69.08% white, indicating that the need for further analysis is necessary if this instrument is to be used in a more diverse student population. We collected validity evidence based upon text content, response processes, and internal structure. Our findings demonstrate that our instrument generates reliable inferences regarding PAD with a Cronbach’s alpha of 0.884 and a six-factor structure that aligns conceptually with the four components of PAD.}, note = {Publisher: American Society for Cell Biology (lse)}, keywords = {}, pubstate = {published}, tppubtype = {article} } Plant awareness disparity (PAD, formerly plant blindness) is the idea that students tend not to notice or appreciate the plants in their environment. This phenomenon often leads to naïve points of view, such as plants are not important or do not do anything for humans. There are four components of PAD: attitude (not liking plants), attention (not noticing plants), knowledge (not understanding the importance of plants), and relative interest (finding animals more interesting than plants). Many interventions have been suggested to prevent PAD, but without an instrument shown to demonstrate valid inferences to measure PAD, it is difficult to tell whether these interventions are successful or not. We have developed and validated the Plant Awareness Disparity Index (PAD-I) to measure PAD and its four components in undergraduate biology students. The study population was 74.32% female and 69.08% white, indicating that the need for further analysis is necessary if this instrument is to be used in a more diverse student population. We collected validity evidence based upon text content, response processes, and internal structure. Our findings demonstrate that our instrument generates reliable inferences regarding PAD with a Cronbach’s alpha of 0.884 and a six-factor structure that aligns conceptually with the four components of PAD. |
Sohini Banerjee; Mazen Istanbouli; Bernie J Daigle Jr. Post-Traumatic Stress Disorder (PTSD) biomarker identification using a deep learning model Journal Article Journal of Emerging Investigators, 5 , pp. 1–8, 2022. @article{banerjee_post-traumatic_2022, title = {Post-Traumatic Stress Disorder (PTSD) biomarker identification using a deep learning model}, author = {Sohini Banerjee and Mazen Istanbouli and Bernie J Daigle Jr.}, url = {https://emerginginvestigators.org/articles/post-traumatic-stress-disorder-ptsd-biomarker-identification-using-a-deep-learning-model}, year = {2022}, date = {2022-09-23}, urldate = {2022-09-30}, journal = {Journal of Emerging Investigators}, volume = {5}, pages = {1--8}, abstract = {Post-traumatic stress disorder (PTSD) is a neuropsychological disorder in which individuals struggle to recover from a traumatizing event. It affects a significant population, including COVID-19 patients, frontline health workers, and war veterans. Given biases associated with self-assessment and diagnosis of PTSD, researchers are actively searching for unbiased biological markers (biomarkers) for predicting PTSD status. The Systems Biology of PTSD Consortium has collected candidate biomarkers for PTSD using molecular and clinical measurements of male war veterans between the ages of 20 and 60. PTSD-positive and negative subjects were separated based on the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) Clinician Administered PTSD Scale (CAPS) scores, derived from structured interviews to measure an individual’s abundance of symptoms, such as re-experiencing, flashbacks, and hyperarousal. CAPS scores higher than 40 were considered PTSD-positive and below 20 were considered negative. We created artificial neural network models to classify PTSD-positive and negative individuals based on metabolomics, micro-RNA (miRNA), protein expression, endocrine markers, and DNA methylation datasets. Model training involved 64 iterations of a Bayesian Hyperparameter Optimization algorithm with 5-fold cross-validation. Each model was calibrated based on cross-validation performance and variance across iterations and then fit to the entire respective dataset (76 PTSD-positive, 76 PTSD-negative). We applied the trained models to an independent validation cohort to assess accuracy on unseen datasets. The top performing datasets from the validation cohort based on classification accuracy were metabolomics (65.2%) and protein expression (61.8%). We anticipate the candidate biomarkers identified in this and future studies will assist with the diagnosis of PTSD.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Post-traumatic stress disorder (PTSD) is a neuropsychological disorder in which individuals struggle to recover from a traumatizing event. It affects a significant population, including COVID-19 patients, frontline health workers, and war veterans. Given biases associated with self-assessment and diagnosis of PTSD, researchers are actively searching for unbiased biological markers (biomarkers) for predicting PTSD status. The Systems Biology of PTSD Consortium has collected candidate biomarkers for PTSD using molecular and clinical measurements of male war veterans between the ages of 20 and 60. PTSD-positive and negative subjects were separated based on the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) Clinician Administered PTSD Scale (CAPS) scores, derived from structured interviews to measure an individual’s abundance of symptoms, such as re-experiencing, flashbacks, and hyperarousal. CAPS scores higher than 40 were considered PTSD-positive and below 20 were considered negative. We created artificial neural network models to classify PTSD-positive and negative individuals based on metabolomics, micro-RNA (miRNA), protein expression, endocrine markers, and DNA methylation datasets. Model training involved 64 iterations of a Bayesian Hyperparameter Optimization algorithm with 5-fold cross-validation. Each model was calibrated based on cross-validation performance and variance across iterations and then fit to the entire respective dataset (76 PTSD-positive, 76 PTSD-negative). We applied the trained models to an independent validation cohort to assess accuracy on unseen datasets. The top performing datasets from the validation cohort based on classification accuracy were metabolomics (65.2%) and protein expression (61.8%). We anticipate the candidate biomarkers identified in this and future studies will assist with the diagnosis of PTSD. |
2021 |
Gwyneth W Y Wu; Owen M Wolkowitz; Victor I Reus; Jee In Kang; Mathea Elnar; Reuben Sarwal; Janine D Flory; Duna Abu-Amara; Rasha Hammanieh; Aarti Gautam; Francis J Doyle; Rachel Yehuda; Charles R Marmar; Marti Jett; Synthia H Mellon; Kerry J Ressler; Ruoting Yang; Seid Muhie; Bernie J Daigle Jr.; Linda M Bierer; Leroy Hood; Kai Wang; Inyoul Lee; Kelsey R Dean; Pramod R Somvanshi pp. 105360, 2021, ISSN: 0306-4530. @article{wu_serum_2021, title = {Serum brain-derived neurotrophic factor remains elevated after long term follow-up of combat veterans with chronic post-traumatic stress disorder}, author = {Gwyneth W Y Wu and Owen M Wolkowitz and Victor I Reus and Jee In Kang and Mathea Elnar and Reuben Sarwal and Janine D Flory and Duna Abu-Amara and Rasha Hammanieh and Aarti Gautam and Francis J Doyle and Rachel Yehuda and Charles R Marmar and Marti Jett and Synthia H Mellon and Kerry J Ressler and Ruoting Yang and Seid Muhie and Bernie J Daigle Jr. and Linda M Bierer and Leroy Hood and Kai Wang and Inyoul Lee and Kelsey R Dean and Pramod R Somvanshi}, url = {https://www.sciencedirect.com/science/article/pii/S0306453021002341}, doi = {10.1016/j.psyneuen.2021.105360}, issn = {0306-4530}, year = {2021}, date = {2021-07-22}, urldate = {2021-08-31}, pages = {105360}, abstract = {Attempts to correlate blood levels of brain-derived neurotrophic factor (BDNF) with post-traumatic stress disorder (PTSD) have provided conflicting results. Some studies found a positive association between BDNF and PTSD diagnosis and symptom severity, while others found the association to be negative. The present study investigated whether serum levels of BDNF are different cross-sectionally between combat trauma-exposed veterans with and without PTSD, as well as whether longitudinal changes in serum BDNF differ as a function of PTSD diagnosis over time. We analyzed data of 270 combat trauma-exposed veterans (230 males, 40 females, average age: 33.29 $pm$ 8.28 years) and found that, at the initial cross-sectional assessment (T0), which averaged 6 years after the initial exposure to combat trauma (SD = 2.83 years), the PTSD positive group had significantly higher serum BDNF levels than the PTSD negative controls [31.03 vs. 26.95ng/mL, t(268) = 3.921, p textless 0.001]. This difference remained significant after excluding individuals with comorbid major depressive disorder, antidepressant users and controlling for age, gender, race, BMI, and time since trauma. Fifty-nine of the male veterans who participated at the first timepoint (T0) were re-assessed at follow-up evaluation (T1), approximately three years (SD = 0.88 years) after T0. A one-way ANOVA comparing PTSD positive, subthreshold PTSD and control groups revealed that serum BDNF remained significantly higher in the PTSD positive group than the control group at T1 [30.05 vs 24.66ng/mL, F(2, 56) = 3.420}, keywords = {}, pubstate = {published}, tppubtype = {article} } Attempts to correlate blood levels of brain-derived neurotrophic factor (BDNF) with post-traumatic stress disorder (PTSD) have provided conflicting results. Some studies found a positive association between BDNF and PTSD diagnosis and symptom severity, while others found the association to be negative. The present study investigated whether serum levels of BDNF are different cross-sectionally between combat trauma-exposed veterans with and without PTSD, as well as whether longitudinal changes in serum BDNF differ as a function of PTSD diagnosis over time. We analyzed data of 270 combat trauma-exposed veterans (230 males, 40 females, average age: 33.29 $pm$ 8.28 years) and found that, at the initial cross-sectional assessment (T0), which averaged 6 years after the initial exposure to combat trauma (SD = 2.83 years), the PTSD positive group had significantly higher serum BDNF levels than the PTSD negative controls [31.03 vs. 26.95ng/mL, t(268) = 3.921, p textless 0.001]. This difference remained significant after excluding individuals with comorbid major depressive disorder, antidepressant users and controlling for age, gender, race, BMI, and time since trauma. Fifty-nine of the male veterans who participated at the first timepoint (T0) were re-assessed at follow-up evaluation (T1), approximately three years (SD = 0.88 years) after T0. A one-way ANOVA comparing PTSD positive, subthreshold PTSD and control groups revealed that serum BDNF remained significantly higher in the PTSD positive group than the control group at T1 [30.05 vs 24.66ng/mL, F(2, 56) = 3.420 |
Carole E Siegel; Eugene M Laska; Ziqiang Lin; Mu Xu; Duna Abu-Amara; Michelle K Jeffers; Meng Qian; Nicholas Milton; Janine D Flory; Rasha Hammamieh; Bernie J Daigle Jr.; Aarti Gautam; Kelsey R Dean; Victor I Reus; Owen M Wolkowitz; Synthia H Mellon; Kerry J Ressler; Rachel Yehuda; Kai Wang; Leroy Hood; Francis J Doyle; Marti Jett; Charles R Marmar Utilization of machine learning for identifying symptom severity military-related PTSD subtypes and their biological correlates Journal Article 11 (1), pp. 1–12, 2021, ISSN: 2158-3188, (Number: 1 Publisher: Nature Publishing Group). @article{siegel_utilization_2021, title = {Utilization of machine learning for identifying symptom severity military-related PTSD subtypes and their biological correlates}, author = {Carole E Siegel and Eugene M Laska and Ziqiang Lin and Mu Xu and Duna Abu-Amara and Michelle K Jeffers and Meng Qian and Nicholas Milton and Janine D Flory and Rasha Hammamieh and Bernie J Daigle Jr. and Aarti Gautam and Kelsey R Dean and Victor I Reus and Owen M Wolkowitz and Synthia H Mellon and Kerry J Ressler and Rachel Yehuda and Kai Wang and Leroy Hood and Francis J Doyle and Marti Jett and Charles R Marmar}, url = {https://www.nature.com/articles/s41398-021-01324-8}, doi = {10.1038/s41398-021-01324-8}, issn = {2158-3188}, year = {2021}, date = {2021-04-20}, urldate = {2021-05-05}, volume = {11}, number = {1}, pages = {1--12}, abstract = {We sought to find clinical subtypes of posttraumatic stress disorder (PTSD) in veterans 6--10 years post-trauma exposure based on current symptom assessments and to examine whether blood biomarkers could differentiate them. Samples were males deployed to Iraq and Afghanistan studied by the PTSD Systems Biology Consortium: a discovery sample of 74 PTSD cases and 71 healthy controls (HC), and a validation sample of 26 PTSD cases and 36 HC. A machine learning method, random forests (RF), in conjunction with a clustering method, partitioning around medoids, were used to identify subtypes derived from 16 self-report and clinician assessment scales, including the clinician-administered PTSD scale for DSM-IV (CAPS). Two subtypes were identified, designated S1 and S2, differing on mean current CAPS total scores: S2 = 75.6 (sd 14.6) and S1 = 54.3 (sd 6.6). S2 had greater symptom severity scores than both S1 and HC on all scale items. The mean first principal component score derived from clinical summary scales was three times higher in S2 than in S1. Distinct RFs were grown to classify S1 and S2 vs. HCs and vs. each other on multi-omic blood markers feature classes of current medical comorbidities, neurocognitive functioning, demographics, pre-military trauma, and psychiatric history. Among these classes, in each RF intergroup comparison of S1, S2, and HC, multi-omic biomarkers yielded the highest AUC-ROCs (0.819--0.922); other classes added little to further discrimination of the subtypes. Among the top five biomarkers in each of these RFs were methylation, micro RNA, and lactate markers, suggesting their biological role in symptom severity.}, note = {Number: 1 Publisher: Nature Publishing Group}, keywords = {}, pubstate = {published}, tppubtype = {article} } We sought to find clinical subtypes of posttraumatic stress disorder (PTSD) in veterans 6--10 years post-trauma exposure based on current symptom assessments and to examine whether blood biomarkers could differentiate them. Samples were males deployed to Iraq and Afghanistan studied by the PTSD Systems Biology Consortium: a discovery sample of 74 PTSD cases and 71 healthy controls (HC), and a validation sample of 26 PTSD cases and 36 HC. A machine learning method, random forests (RF), in conjunction with a clustering method, partitioning around medoids, were used to identify subtypes derived from 16 self-report and clinician assessment scales, including the clinician-administered PTSD scale for DSM-IV (CAPS). Two subtypes were identified, designated S1 and S2, differing on mean current CAPS total scores: S2 = 75.6 (sd 14.6) and S1 = 54.3 (sd 6.6). S2 had greater symptom severity scores than both S1 and HC on all scale items. The mean first principal component score derived from clinical summary scales was three times higher in S2 than in S1. Distinct RFs were grown to classify S1 and S2 vs. HCs and vs. each other on multi-omic blood markers feature classes of current medical comorbidities, neurocognitive functioning, demographics, pre-military trauma, and psychiatric history. Among these classes, in each RF intergroup comparison of S1, S2, and HC, multi-omic biomarkers yielded the highest AUC-ROCs (0.819--0.922); other classes added little to further discrimination of the subtypes. Among the top five biomarkers in each of these RFs were methylation, micro RNA, and lactate markers, suggesting their biological role in symptom severity. |
Liangqun Lu; Kevin A Townsend; Bernie J Daigle Jr. GEOlimma: differential expression analysis and feature selection using pre-existing microarray data Journal Article 22 (1), pp. 44, 2021, ISSN: 1471-2105. @article{lu_geolimma_2021, title = {GEOlimma: differential expression analysis and feature selection using pre-existing microarray data}, author = {Liangqun Lu and Kevin A Townsend and Bernie J Daigle Jr.}, url = {https://doi.org/10.1186/s12859-020-03932-5}, doi = {10.1186/s12859-020-03932-5}, issn = {1471-2105}, year = {2021}, date = {2021-02-03}, urldate = {2021-08-31}, volume = {22}, number = {1}, pages = {44}, abstract = {Differential expression and feature selection analyses are essential steps for the development of accurate diagnostic/prognostic classifiers of complicated human diseases using transcriptomics data. These steps are particularly challenging due to the curse of dimensionality and the presence of technical and biological noise. A promising strategy for overcoming these challenges is the incorporation of pre-existing transcriptomics data in the identification of differentially expressed (DE) genes. This approach has the potential to improve the quality of selected genes, increase classification performance, and enhance biological interpretability. While a number of methods have been developed that use pre-existing data for differential expression analysis, existing methods do not leverage the identities of experimental conditions to create a robust metric for identifying DE genes.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Differential expression and feature selection analyses are essential steps for the development of accurate diagnostic/prognostic classifiers of complicated human diseases using transcriptomics data. These steps are particularly challenging due to the curse of dimensionality and the presence of technical and biological noise. A promising strategy for overcoming these challenges is the incorporation of pre-existing transcriptomics data in the identification of differentially expressed (DE) genes. This approach has the potential to improve the quality of selected genes, increase classification performance, and enhance biological interpretability. While a number of methods have been developed that use pre-existing data for differential expression analysis, existing methods do not leverage the identities of experimental conditions to create a robust metric for identifying DE genes. |
2020 |
Ruoting Yang; Aarti Gautam; Derese Getnet; Bernie J Daigle Jr.; Stacy Miller; Burook Misganaw; Kelsey R Dean; Raina Kumar; Seid Muhie; Kai Wang; Inyoul Lee; Duna Abu-Amara; Janine D Flory; Leroy Hood; Owen M Wolkowitz; Synthia H Mellon; Francis J Doyle; Rachel Yehuda; Charles R Marmar; Kerry J Ressler; Rasha Hammamieh; Marti Jett Epigenetic biotypes of post-traumatic stress disorder in war-zone exposed veteran and active duty males Journal Article pp. 1–15, 2020, ISSN: 1476-5578, (Publisher: Nature Publishing Group). @article{yang_epigenetic_2020, title = {Epigenetic biotypes of post-traumatic stress disorder in war-zone exposed veteran and active duty males}, author = {Ruoting Yang and Aarti Gautam and Derese Getnet and Bernie J Daigle Jr. and Stacy Miller and Burook Misganaw and Kelsey R Dean and Raina Kumar and Seid Muhie and Kai Wang and Inyoul Lee and Duna Abu-Amara and Janine D Flory and Leroy Hood and Owen M Wolkowitz and Synthia H Mellon and Francis J Doyle and Rachel Yehuda and Charles R Marmar and Kerry J Ressler and Rasha Hammamieh and Marti Jett}, url = {https://www.nature.com/articles/s41380-020-00966-2}, doi = {10.1038/s41380-020-00966-2}, issn = {1476-5578}, year = {2020}, date = {2020-12-18}, urldate = {2021-05-05}, pages = {1--15}, abstract = {Post-traumatic stress disorder (PTSD) is a heterogeneous condition evidenced by the absence of objective physiological measurements applicable to all who meet the criteria for the disorder as well as divergent responses to treatments. This study capitalized on biological diversity observed within the PTSD group observed following epigenome-wide analysis of a well-characterized Discovery cohort (N = 166) consisting of 83 male combat exposed veterans with PTSD, and 83 combat veterans without PTSD in order to identify patterns that might distinguish subtypes. Computational analysis of DNA methylation (DNAm) profiles identified two PTSD biotypes within the PTSD+ group, G1 and G2, associated with 34 clinical features that are associated with PTSD and PTSD comorbidities. The G2 biotype was associated with an increased PTSD risk and had higher polygenic risk scores and a greater methylation compared to the G1 biotype and healthy controls. The findings were validated at a 3-year follow-up (N = 59) of the same individuals as well as in two independent, veteran cohorts (N = 54 and N = 38), and an active duty cohort (N = 133). In some cases, for example Dopamine-PKA-CREB and GABA-PKC-CREB signaling pathways, the biotypes were oppositely dysregulated, suggesting that the biotypes were not simply a function of a dimensional relationship with symptom severity, but may represent distinct biological risk profiles underpinning PTSD. The identification of two novel distinct epigenetic biotypes for PTSD may have future utility in understanding biological and clinical heterogeneity in PTSD and potential applications in risk assessment for active duty military personnel under non-clinician-administered settings, and improvement of PTSD diagnostic markers.}, note = {Publisher: Nature Publishing Group}, keywords = {}, pubstate = {published}, tppubtype = {article} } Post-traumatic stress disorder (PTSD) is a heterogeneous condition evidenced by the absence of objective physiological measurements applicable to all who meet the criteria for the disorder as well as divergent responses to treatments. This study capitalized on biological diversity observed within the PTSD group observed following epigenome-wide analysis of a well-characterized Discovery cohort (N = 166) consisting of 83 male combat exposed veterans with PTSD, and 83 combat veterans without PTSD in order to identify patterns that might distinguish subtypes. Computational analysis of DNA methylation (DNAm) profiles identified two PTSD biotypes within the PTSD+ group, G1 and G2, associated with 34 clinical features that are associated with PTSD and PTSD comorbidities. The G2 biotype was associated with an increased PTSD risk and had higher polygenic risk scores and a greater methylation compared to the G1 biotype and healthy controls. The findings were validated at a 3-year follow-up (N = 59) of the same individuals as well as in two independent, veteran cohorts (N = 54 and N = 38), and an active duty cohort (N = 133). In some cases, for example Dopamine-PKA-CREB and GABA-PKC-CREB signaling pathways, the biotypes were oppositely dysregulated, suggesting that the biotypes were not simply a function of a dimensional relationship with symptom severity, but may represent distinct biological risk profiles underpinning PTSD. The identification of two novel distinct epigenetic biotypes for PTSD may have future utility in understanding biological and clinical heterogeneity in PTSD and potential applications in risk assessment for active duty military personnel under non-clinician-administered settings, and improvement of PTSD diagnostic markers. |
Jee In Kang; Susanne G. Mueller; Gwyneth W. Y. Wu; Jue Lin; Peter Ng; Rachel Yehuda; Janine D. Flory; Duna Abu-Amara; Victor I. Reus; Aarti Gautam; Leroy Hood; Kerry J. Ressler; Daniel Lindqvist; Ji Hoon Cho; Michelle Coy; Frank Desarnaud; Saverio Bersani; Silvia Fossati; Allison Hoke; Raina Kumar; Meng Li; Iouri Makotkine; Stacy-Ann Miller; Linda Petzold; Laura Price; Meng Qian; Kelsey Scherler; Seshamalini Srinivasan; Anna Suessbrick; Li Tang; Xiaogang Wu; David Baxter; Esther Blessing; Kelsey R. Dean; Bernie J. Daigle Jr.; Guia Guffanti; Kai Wang; Lynn M. Almli; F. Nabarun Chakraborty; Duncan Donohue; Kimberly Kerley; Taek-Kyun Kim; Eugene Laska; Inyoul Lee; Min Young Lee; Adriana Lori; Liangqun Lu; Burook Misganaw; Seid Muhie; Jennifer Newman; Nathan Price; Shizhen Qin; Carole Siegel; Pramod R. Somvanshi; Gunjan S. Thakur; Young Zhou; Ruoting Yang; Rasha Hammamieh; Francis J. Doyle; Marti Jett; Charles R. Marmar; Synthia H. Mellon; Owen M. Wolkowitz Effect of Combat Exposure and Posttraumatic Stress Disorder on Telomere Length and Amygdala Volume Journal Article Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 5 (7), pp. 678–687, 2020, ISSN: 2451-9022. @article{kang_effect_2020, title = {Effect of Combat Exposure and Posttraumatic Stress Disorder on Telomere Length and Amygdala Volume}, author = { Jee In Kang and Susanne G. Mueller and Gwyneth W. Y. Wu and Jue Lin and Peter Ng and Rachel Yehuda and Janine D. Flory and Duna Abu-Amara and Victor I. Reus and Aarti Gautam and Leroy Hood and Kerry J. Ressler and Daniel Lindqvist and Ji Hoon Cho and Michelle Coy and Frank Desarnaud and Saverio Bersani and Silvia Fossati and Allison Hoke and Raina Kumar and Meng Li and Iouri Makotkine and Stacy-Ann Miller and Linda Petzold and Laura Price and Meng Qian and Kelsey Scherler and Seshamalini Srinivasan and Anna Suessbrick and Li Tang and Xiaogang Wu and David Baxter and Esther Blessing and Kelsey R. Dean and Bernie J. Daigle Jr. and Guia Guffanti and Kai Wang and Lynn M. Almli and F. Nabarun Chakraborty and Duncan Donohue and Kimberly Kerley and Taek-Kyun Kim and Eugene Laska and Inyoul Lee and Min Young Lee and Adriana Lori and Liangqun Lu and Burook Misganaw and Seid Muhie and Jennifer Newman and Nathan Price and Shizhen Qin and Carole Siegel and Pramod R. Somvanshi and Gunjan S. Thakur and Young Zhou and Ruoting Yang and Rasha Hammamieh and Francis J. Doyle and Marti Jett and Charles R. Marmar and Synthia H. Mellon and Owen M. Wolkowitz}, url = {http://www.sciencedirect.com/science/article/pii/S2451902220300768}, doi = {10.1016/j.bpsc.2020.03.007}, issn = {2451-9022}, year = {2020}, date = {2020-07-01}, urldate = {2020-08-31}, journal = {Biological Psychiatry: Cognitive Neuroscience and Neuroimaging}, volume = {5}, number = {7}, pages = {678--687}, abstract = {Background Traumatic stress can adversely affect physical and mental health through neurobiological stress response systems. We examined the effects of trauma exposure and posttraumatic stress disorder (PTSD) on telomere length, a biomarker of cellular aging, and volume of the amygdala, a key structure of stress regulation, in combat-exposed veterans. In addition, the relationships of psychopathological symptoms and autonomic function with telomere length and amygdala volume were examined. Methods Male combat veterans were categorized as having PTSD diagnosis (n = 102) or no lifetime PTSD diagnosis (n = 111) based on the Clinician-Administered PTSD Scale. Subjects were assessed for stress-related psychopathology, trauma severity, autonomic function, and amygdala volumes by magnetic resonance imaging. Results A significant interaction was found between trauma severity and PTSD status for telomere length and amygdala volume after adjusting for multiple confounders. Subjects with PTSD showed shorter telomere length and larger amygdala volume than those without PTSD among veterans exposed to high trauma, while there was no significant group difference in these parameters among those exposed to low trauma. Among veterans exposed to high trauma, greater telomere shortening was significantly correlated with greater norepinephrine, and larger amygdala volume was correlated with more severe psychological symptoms and higher heart rates. Conclusions These data suggest that the intensity of the index trauma event plays an important role in interacting with PTSD symptomatology and autonomic activity in predicting telomere length and amygdala volume. These results highlight the importance of trauma severity and PTSD status in predicting certain biological outcomes.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Background Traumatic stress can adversely affect physical and mental health through neurobiological stress response systems. We examined the effects of trauma exposure and posttraumatic stress disorder (PTSD) on telomere length, a biomarker of cellular aging, and volume of the amygdala, a key structure of stress regulation, in combat-exposed veterans. In addition, the relationships of psychopathological symptoms and autonomic function with telomere length and amygdala volume were examined. Methods Male combat veterans were categorized as having PTSD diagnosis (n = 102) or no lifetime PTSD diagnosis (n = 111) based on the Clinician-Administered PTSD Scale. Subjects were assessed for stress-related psychopathology, trauma severity, autonomic function, and amygdala volumes by magnetic resonance imaging. Results A significant interaction was found between trauma severity and PTSD status for telomere length and amygdala volume after adjusting for multiple confounders. Subjects with PTSD showed shorter telomere length and larger amygdala volume than those without PTSD among veterans exposed to high trauma, while there was no significant group difference in these parameters among those exposed to low trauma. Among veterans exposed to high trauma, greater telomere shortening was significantly correlated with greater norepinephrine, and larger amygdala volume was correlated with more severe psychological symptoms and higher heart rates. Conclusions These data suggest that the intensity of the index trauma event plays an important role in interacting with PTSD symptomatology and autonomic activity in predicting telomere length and amygdala volume. These results highlight the importance of trauma severity and PTSD status in predicting certain biological outcomes. |
Liangqun Lu; Bernie J. Daigle Jr. Multi-Omic PTSD Subgroup Identification and Clinical Characterization Journal Article Biological Psychiatry, 87 (9), pp. S9, 2020, ISSN: 0006-3223, 1873-2402. @article{luMultiOmicPTSDSubgroup2020, title = {Multi-Omic PTSD Subgroup Identification and Clinical Characterization}, author = { Liangqun Lu and Bernie J. Daigle Jr.}, doi = {10.1016/j.biopsych.2020.02.050}, issn = {0006-3223, 1873-2402}, year = {2020}, date = {2020-05-01}, journal = {Biological Psychiatry}, volume = {87}, number = {9}, pages = {S9}, publisher = {Elsevier}, abstract = {Post-Traumatic Stress Disorder (PTSD) is a psychiatric disorder caused by environmental and genetic factors. Manifestation of PTSD can be highly variable; thus, efforts have focused on identifying subgroups of the disorder. Much of this work involves categorizing symptoms of PTSD in combination with personality traits and trauma types. Analysis of ``omics'' data thus provides a unique opportunity to identify molecular subgroups of PTSD.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Post-Traumatic Stress Disorder (PTSD) is a psychiatric disorder caused by environmental and genetic factors. Manifestation of PTSD can be highly variable; thus, efforts have focused on identifying subgroups of the disorder. Much of this work involves categorizing symptoms of PTSD in combination with personality traits and trauma types. Analysis of ``omics'' data thus provides a unique opportunity to identify molecular subgroups of PTSD. |
Ruoting Yang; Aarti Gautam; Derese Getnet; Bernie J. Daigle Jr.; Stacy Ann Miller; Kelsey Dean; Seid Muhie; Kai Wang; Inyoul Lee; Duna Abu Amara; Janine D. Flory; Leroy Hood; Owen Wolkowitz; Synthia Mellon; Francis J. Doyle; Rachel Yehuda; Charles Marmar; Kerry Ressler; Rasha Hammamieh; Marti Jett Epigenetic Biotypes of PTSD in War-Zone Exposed Veteran and Active Duty Males Journal Article Biological Psychiatry, 87 (9), pp. S8-S9, 2020, ISSN: 0006-3223, 1873-2402. @article{yangEpigeneticBiotypesPTSD2020, title = {Epigenetic Biotypes of PTSD in War-Zone Exposed Veteran and Active Duty Males}, author = { Ruoting Yang and Aarti Gautam and Derese Getnet and Bernie J. Daigle Jr. and Stacy Ann Miller and Kelsey Dean and Seid Muhie and Kai Wang and Inyoul Lee and Duna Abu Amara and Janine D. Flory and Leroy Hood and Owen Wolkowitz and Synthia Mellon and Francis J. Doyle and Rachel Yehuda and Charles Marmar and Kerry Ressler and Rasha Hammamieh and Marti Jett}, doi = {10.1016/j.biopsych.2020.02.049}, issn = {0006-3223, 1873-2402}, year = {2020}, date = {2020-05-01}, journal = {Biological Psychiatry}, volume = {87}, number = {9}, pages = {S8-S9}, publisher = {Elsevier}, abstract = {That PTSD is a heterogeneous condition is supported by both the failure to identify objective physiological measurements applicable to all who meet criteria for the disorder, and divergent responses associated with PTSD treatments.}, keywords = {}, pubstate = {published}, tppubtype = {article} } That PTSD is a heterogeneous condition is supported by both the failure to identify objective physiological measurements applicable to all who meet criteria for the disorder, and divergent responses associated with PTSD treatments. |
Liangqun Lu; Bernie J. Daigle Jr. Prognostic Analysis of Histopathological Images Using Pre-Trained Convolutional Neural Networks: Application to Hepatocellular Carcinoma Journal Article PeerJ, 8 , pp. e8668, 2020, ISSN: 2167-8359. @article{luPrognosticAnalysisHistopathological2020, title = {Prognostic Analysis of Histopathological Images Using Pre-Trained Convolutional Neural Networks: Application to Hepatocellular Carcinoma}, author = { Liangqun Lu and Bernie J. Daigle Jr.}, doi = {10.7717/peerj.8668}, issn = {2167-8359}, year = {2020}, date = {2020-03-12}, journal = {PeerJ}, volume = {8}, pages = {e8668}, publisher = {PeerJ Inc.}, abstract = {Histopathological images contain rich phenotypic descriptions of the molecular processes underlying disease progression. Convolutional neural networks, state-of-the-art image analysis techniques in computer vision, automatically learn representative features from such images which can be useful for disease diagnosis, prognosis, and subtyping. Hepatocellular carcinoma (HCC) is the sixth most common type of primary liver malignancy. Despite the high mortality rate of HCC, little previous work has made use of CNN models to explore the use of histopathological images for prognosis and clinical survival prediction of HCC. We applied three pre-trained CNN models—VGG 16, Inception V3 and ResNet 50—to extract features from HCC histopathological images. Sample visualization and classification analyses based on these features showed a very clear separation between cancer and normal samples. In a univariate Cox regression analysis, 21.4% and 16% of image features on average were significantly associated with overall survival (OS) and disease-free survival (DFS), respectively. We also observed significant correlations between these features and integrated biological pathways derived from gene expression and copy number variation. Using an elastic net regularized Cox Proportional Hazards model of OS constructed from Inception image features, we obtained a concordance index (C-index) of 0.789 and a significant log-rank test (p = 7.6E-18). We also performed unsupervised classification to identify HCC subgroups from image features. The optimal two subgroups discovered using Inception model image features showed significant differences in both overall (C-index = 0.628 and p = 7.39E-07) and DFS (C-index = 0.558 and p = 0.012). Our work demonstrates the utility of extracting image features using pre-trained models by using them to build accurate prognostic models of HCC as well as highlight significant correlations between these features, clinical survival, and relevant biological pathways. Image features extracted from HCC histopathological images using the pre-trained CNN models VGG 16, Inception V3 and ResNet 50 can accurately distinguish normal and cancer samples. Furthermore, these image features are significantly correlated with survival and relevant biological pathways.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Histopathological images contain rich phenotypic descriptions of the molecular processes underlying disease progression. Convolutional neural networks, state-of-the-art image analysis techniques in computer vision, automatically learn representative features from such images which can be useful for disease diagnosis, prognosis, and subtyping. Hepatocellular carcinoma (HCC) is the sixth most common type of primary liver malignancy. Despite the high mortality rate of HCC, little previous work has made use of CNN models to explore the use of histopathological images for prognosis and clinical survival prediction of HCC. We applied three pre-trained CNN models—VGG 16, Inception V3 and ResNet 50—to extract features from HCC histopathological images. Sample visualization and classification analyses based on these features showed a very clear separation between cancer and normal samples. In a univariate Cox regression analysis, 21.4% and 16% of image features on average were significantly associated with overall survival (OS) and disease-free survival (DFS), respectively. We also observed significant correlations between these features and integrated biological pathways derived from gene expression and copy number variation. Using an elastic net regularized Cox Proportional Hazards model of OS constructed from Inception image features, we obtained a concordance index (C-index) of 0.789 and a significant log-rank test (p = 7.6E-18). We also performed unsupervised classification to identify HCC subgroups from image features. The optimal two subgroups discovered using Inception model image features showed significant differences in both overall (C-index = 0.628 and p = 7.39E-07) and DFS (C-index = 0.558 and p = 0.012). Our work demonstrates the utility of extracting image features using pre-trained models by using them to build accurate prognostic models of HCC as well as highlight significant correlations between these features, clinical survival, and relevant biological pathways. Image features extracted from HCC histopathological images using the pre-trained CNN models VGG 16, Inception V3 and ResNet 50 can accurately distinguish normal and cancer samples. Furthermore, these image features are significantly correlated with survival and relevant biological pathways. |
2019 |
Vinhthuy Phan; Diem-Trang Pham; Caroline Melton; Adam J. Ramsey; Bernie J. Daigle Jr.; Jennifer R. Mandel icHET: Interactive Visualization of Cytoplasmic Heteroplasmy Journal Article Bioinformatics, 35 (21), pp. 4411-4412, 2019, ISSN: 1367-4803. @article{phanIcHETInteractiveVisualization2019, title = {icHET: Interactive Visualization of Cytoplasmic Heteroplasmy}, author = { Vinhthuy Phan and Diem-Trang Pham and Caroline Melton and Adam J. Ramsey and Bernie J. Daigle Jr. and Jennifer R. Mandel}, doi = {10.1093/bioinformatics/btz300}, issn = {1367-4803}, year = {2019}, date = {2019-11-01}, journal = {Bioinformatics}, volume = {35}, number = {21}, pages = {4411-4412}, abstract = {AbstractSummary. Although heteroplasmy has been studied extensively in animal systems, there is a lack of tools for analyzing, exploring and visualizing hetero}, keywords = {}, pubstate = {published}, tppubtype = {article} } AbstractSummary. Although heteroplasmy has been studied extensively in animal systems, there is a lack of tools for analyzing, exploring and visualizing hetero |
Kelsey R. Dean; Rasha Hammamieh; Synthia H. Mellon; Duna Abu-Amara; Janine D. Flory; Guia Guffanti; Kai Wang; Bernie J. Daigle Jr.; Aarti Gautam; Inyoul Lee; Ruoting Yang; Lynn M. Almli; F. Saverio Bersani; Nabarun Chakraborty; Duncan Donohue; Kimberly Kerley; Taek-Kyun Kim; Eugene Laska; Min Young Lee; Daniel Lindqvist; Adriana Lori; Liangqun Lu; Burook Misganaw; Seid Muhie; Jennifer Newman; Nathan D. Price; Shizhen Qin; Victor I. Reus; Carole Siegel; Pramod R. Somvanshi; Gunjan S. Thakur; Yong Zhou; Leroy Hood; Kerry J. Ressler; Owen M. Wolkowitz; Rachel Yehuda; Marti Jett; Francis J. Doyle; Charles Marmar Multi-Omic Biomarker Identification and Validation for Diagnosing Warzone-Related Post-Traumatic Stress Disorder Journal Article Molecular Psychiatry, pp. 1-13, 2019, ISSN: 1476-5578. @article{deanMultiomicBiomarkerIdentification2019, title = {Multi-Omic Biomarker Identification and Validation for Diagnosing Warzone-Related Post-Traumatic Stress Disorder}, author = { Kelsey R. Dean and Rasha Hammamieh and Synthia H. Mellon and Duna {Abu-Amara} and Janine D. Flory and Guia Guffanti and Kai Wang and Bernie J. Daigle Jr. and Aarti Gautam and Inyoul Lee and Ruoting Yang and Lynn M. Almli and F. Saverio Bersani and Nabarun Chakraborty and Duncan Donohue and Kimberly Kerley and Taek-Kyun Kim and Eugene Laska and Min Young Lee and Daniel Lindqvist and Adriana Lori and Liangqun Lu and Burook Misganaw and Seid Muhie and Jennifer Newman and Nathan D. Price and Shizhen Qin and Victor I. Reus and Carole Siegel and Pramod R. Somvanshi and Gunjan S. Thakur and Yong Zhou and Leroy Hood and Kerry J. Ressler and Owen M. Wolkowitz and Rachel Yehuda and Marti Jett and Francis J. Doyle and Charles Marmar}, doi = {10.1038/s41380-019-0496-z}, issn = {1476-5578}, year = {2019}, date = {2019-09-10}, journal = {Molecular Psychiatry}, pages = {1-13}, abstract = {Post-traumatic stress disorder (PTSD) impacts many veterans and active duty soldiers, but diagnosis can be problematic due to biases in self-disclosure of symptoms, stigma within military populations, and limitations identifying those at risk. Prior studies suggest that PTSD may be a systemic illness, affecting not just the brain, but the entire body. Therefore, disease signals likely span multiple biological domains, including genes, proteins, cells, tissues, and organism-level physiological changes. Identification of these signals could aid in diagnostics, treatment decision-making, and risk evaluation. In the search for PTSD diagnostic biomarkers, we ascertained over one million molecular, cellular, physiological, and clinical features from three cohorts of male veterans. In a discovery cohort of 83 warzone-related PTSD cases and 82 warzone-exposed controls, we identified a set of 343 candidate biomarkers. These candidate biomarkers were selected from an integrated approach using (1) data-driven methods, including Support Vector Machine with Recursive Feature Elimination and other standard or published methodologies, and (2) hypothesis-driven approaches, using previous genetic studies for polygenic risk, or other PTSD-related literature. After reassessment of textasciitilde30% of these participants, we refined this set of markers from 343 to 28, based on their performance and ability to track changes in phenotype over time. The final diagnostic panel of 28 features was validated in an independent cohort (26 cases, 26 controls) with good performance (AUC,=,0.80, 81% accuracy, 85% sensitivity, and 77% specificity). The identification and validation of this diverse diagnostic panel represents a powerful and novel approach to improve accuracy and reduce bias in diagnosing combat-related PTSD.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Post-traumatic stress disorder (PTSD) impacts many veterans and active duty soldiers, but diagnosis can be problematic due to biases in self-disclosure of symptoms, stigma within military populations, and limitations identifying those at risk. Prior studies suggest that PTSD may be a systemic illness, affecting not just the brain, but the entire body. Therefore, disease signals likely span multiple biological domains, including genes, proteins, cells, tissues, and organism-level physiological changes. Identification of these signals could aid in diagnostics, treatment decision-making, and risk evaluation. In the search for PTSD diagnostic biomarkers, we ascertained over one million molecular, cellular, physiological, and clinical features from three cohorts of male veterans. In a discovery cohort of 83 warzone-related PTSD cases and 82 warzone-exposed controls, we identified a set of 343 candidate biomarkers. These candidate biomarkers were selected from an integrated approach using (1) data-driven methods, including Support Vector Machine with Recursive Feature Elimination and other standard or published methodologies, and (2) hypothesis-driven approaches, using previous genetic studies for polygenic risk, or other PTSD-related literature. After reassessment of textasciitilde30% of these participants, we refined this set of markers from 343 to 28, based on their performance and ability to track changes in phenotype over time. The final diagnostic panel of 28 features was validated in an independent cohort (26 cases, 26 controls) with good performance (AUC,=,0.80, 81% accuracy, 85% sensitivity, and 77% specificity). The identification and validation of this diverse diagnostic panel represents a powerful and novel approach to improve accuracy and reduce bias in diagnosing combat-related PTSD. |
Pramod R. Somvanshi; Synthia H. Mellon; Janine D. Flory; Duna Abu-amara; Rasha Hammamieh; Aarti Gautam; Kai Wang; Inyoul Lee; Bernie J. Daigle Jr.; Ruoting Yang; Owen M. Wolkowitz; Rachel Yehuda; Marti Jett; Leroy Hood; Charles Marmar; Francis J. Doyle American Journal of Physiology-Endocrinology and Metabolism, 2019, ISSN: 0193-1849. @article{somvanshiMechanisticInferencesMetabolic2019, title = {Mechanistic Inferences on Metabolic Dysfunction in PTSD from an Integrated Model and Multi-Omic Analysis: Role of Glucocorticoid Receptor Sensitivity}, author = { Pramod R. Somvanshi and Synthia H. Mellon and Janine D. Flory and Duna {Abu-amara} and Rasha Hammamieh and Aarti Gautam and Kai Wang and Inyoul Lee and Bernie J. Daigle Jr. and Ruoting Yang and Owen M. Wolkowitz and Rachel Yehuda and Marti Jett and Leroy Hood and Charles Marmar and Francis J. Doyle}, doi = {10.1152/ajpendo.00065.2019}, issn = {0193-1849}, year = {2019}, date = {2019-07-19}, journal = {American Journal of Physiology-Endocrinology and Metabolism}, abstract = {Post-traumatic stress disorder is associated with neuroendocrine alterations and metabolic abnormalities; however, how metabolism is affected by neuroendocrine disturbances is unclear. The data from combat exposed veterans with PTSD shows increased glycolysis to lactate flux, reduced TCA cycle flux, impaired amino acid and lipid metabolism, insulin resistance, inflammation and hypersensitive HPA-axis. To analyze whether the co-occurrence of multiple metabolic abnormalities are independent, or arises from an underlying regulatory defect, we employed a systems biological approach using an integrated mathematical model and multi-omic analysis. The models for hepatic metabolism, HPA axis, inflammation and regulatory signaling were integrated to perform metabolic control analysis (MCA) with respect to the observations from our data. We combined the metabolomics, neuroendocrine, clinical lab and cytokine data from combat-exposed veterans with and without PTSD to characterize the differences in regulatory effects. MCA revealed mechanistic association of the HPA-axis and inflammation with metabolic dysfunction consistent with PTSD. This was supported by the data using correlational and causal analysis that revealed significant associations between cortisol suppression, hs-CRP, HOMAIR, GGT, hypoxanthine and several metabolites. Causal mediation analysis indicates that the effects of enhanced glucocorticoid receptor sensitivity (GRS) on glycolytic pathway, gluconeogenic and branched chain amino acids, triglycerides and hepatic function are jointly mediated by inflammation, insulin resistance, oxidative stress and energy deficit. Our analysis suggests that the interventions to normalize GRS and inflammation may help to manage features of metabolic dysfunction in PTSD.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Post-traumatic stress disorder is associated with neuroendocrine alterations and metabolic abnormalities; however, how metabolism is affected by neuroendocrine disturbances is unclear. The data from combat exposed veterans with PTSD shows increased glycolysis to lactate flux, reduced TCA cycle flux, impaired amino acid and lipid metabolism, insulin resistance, inflammation and hypersensitive HPA-axis. To analyze whether the co-occurrence of multiple metabolic abnormalities are independent, or arises from an underlying regulatory defect, we employed a systems biological approach using an integrated mathematical model and multi-omic analysis. The models for hepatic metabolism, HPA axis, inflammation and regulatory signaling were integrated to perform metabolic control analysis (MCA) with respect to the observations from our data. We combined the metabolomics, neuroendocrine, clinical lab and cytokine data from combat-exposed veterans with and without PTSD to characterize the differences in regulatory effects. MCA revealed mechanistic association of the HPA-axis and inflammation with metabolic dysfunction consistent with PTSD. This was supported by the data using correlational and causal analysis that revealed significant associations between cortisol suppression, hs-CRP, HOMAIR, GGT, hypoxanthine and several metabolites. Causal mediation analysis indicates that the effects of enhanced glucocorticoid receptor sensitivity (GRS) on glycolytic pathway, gluconeogenic and branched chain amino acids, triglycerides and hepatic function are jointly mediated by inflammation, insulin resistance, oxidative stress and energy deficit. Our analysis suggests that the interventions to normalize GRS and inflammation may help to manage features of metabolic dysfunction in PTSD. |
Liangqun Lu; Kevin A. Townsend; Bernie J. Daigle Jr. GEOlimma: Differential Expression Analysis and Feature Selection Using Pre-Existing Microarray Data Journal Article bioRxiv, pp. 693564, 2019. @article{luGEOlimmaDifferentialExpression2019, title = {GEOlimma: Differential Expression Analysis and Feature Selection Using Pre-Existing Microarray Data}, author = { Liangqun Lu and Kevin A. Townsend and Bernie J. Daigle Jr.}, doi = {10.1101/693564}, year = {2019}, date = {2019-07-05}, journal = {bioRxiv}, pages = {693564}, publisher = {Cold Spring Harbor Laboratory}, chapter = {New Results}, abstract = {$<$h3$>$Abstract$<$/h3$>$ $<$h3$>$Background$<$/h3$>$ $<$p$>$Differential expression and feature selection analyses are essential steps for the development of accurate diagnostic/prognostic classifiers of complicated human diseases using transcriptomics data. These steps are particularly challenging due to the curse of dimensionality and the presence of technical and biological noise. A promising strategy for overcoming these challenges is the incorporation of pre-existing transcriptomics data in the identification of differentially expressed (DE) genes. This approach has the potential to improve the quality of selected genes, increase classification performance, and enhance biological interpretability. While a number of methods have been developed that use pre-existing data for differential expression analysis, existing methods do not leverage the identities of experimental conditions to create a robust metric for identifying DE genes.$<$/p$><$h3$>$Results$<$/h3$>$ $<$p$>$In this study, we propose a novel differential expression and feature selection method— GEOlimma— which combines pre-existing microarray data from the Gene Expression Omnibus (GEO) with the widely-applied Limma method for differential expression analysis. We first quantify differential gene expression across 2481 pairwise comparisons from 602 curated GEO Datasets, and we convert differential expression frequencies to DE prior probabilities. Genes with high DE prior probabilities show enrichment in cell growth and death, signal transduction, and cancer-related biological pathways, while genes with low prior probabilities were enriched in sensory system pathways. We then applied GEOlimma to four differential expression comparisons within two human disease datasets and performed differential expression, feature selection, and supervised classification analyses. Our results suggest that use of GEOlimma provides greater experimental power to detect DE genes compared to Limma, due to its increased effective sample size. Furthermore, in a supervised classification analysis using GEOlimma as a feature selection method, we observed similar or better classification performance than Limma given small, noisy subsets of an asthma dataset.$<$/p$><$h3$>$Conclusions$<$/h3$>$ $<$p$>$Our results demonstrate that GEOlimma is a more effective method for differential gene expression and feature selection analyses compared to the standard Limma method. Due to its focus on gene-level differential expression, GEOlimma also has the potential to be applied to other high-throughput biological datasets.$<$/p$>$}, keywords = {}, pubstate = {published}, tppubtype = {article} } $<$h3$>$Abstract$<$/h3$>$ $<$h3$>$Background$<$/h3$>$ $<$p$>$Differential expression and feature selection analyses are essential steps for the development of accurate diagnostic/prognostic classifiers of complicated human diseases using transcriptomics data. These steps are particularly challenging due to the curse of dimensionality and the presence of technical and biological noise. A promising strategy for overcoming these challenges is the incorporation of pre-existing transcriptomics data in the identification of differentially expressed (DE) genes. This approach has the potential to improve the quality of selected genes, increase classification performance, and enhance biological interpretability. While a number of methods have been developed that use pre-existing data for differential expression analysis, existing methods do not leverage the identities of experimental conditions to create a robust metric for identifying DE genes.$<$/p$><$h3$>$Results$<$/h3$>$ $<$p$>$In this study, we propose a novel differential expression and feature selection method— GEOlimma— which combines pre-existing microarray data from the Gene Expression Omnibus (GEO) with the widely-applied Limma method for differential expression analysis. We first quantify differential gene expression across 2481 pairwise comparisons from 602 curated GEO Datasets, and we convert differential expression frequencies to DE prior probabilities. Genes with high DE prior probabilities show enrichment in cell growth and death, signal transduction, and cancer-related biological pathways, while genes with low prior probabilities were enriched in sensory system pathways. We then applied GEOlimma to four differential expression comparisons within two human disease datasets and performed differential expression, feature selection, and supervised classification analyses. Our results suggest that use of GEOlimma provides greater experimental power to detect DE genes compared to Limma, due to its increased effective sample size. Furthermore, in a supervised classification analysis using GEOlimma as a feature selection method, we observed similar or better classification performance than Limma given small, noisy subsets of an asthma dataset.$<$/p$><$h3$>$Conclusions$<$/h3$>$ $<$p$>$Our results demonstrate that GEOlimma is a more effective method for differential gene expression and feature selection analyses compared to the standard Limma method. Due to its focus on gene-level differential expression, GEOlimma also has the potential to be applied to other high-throughput biological datasets.$<$/p$>$ |
Burook Misganaw; Guia Guffanti; Adriana Lori; Duna Abu-Amara; Janine D. Flory; Rasha Hammamieh; Aarti Gautam; Ruoting Yang; Bernie J. Daigle Jr.; Leroy Hood; Kai Wang; Inyoul Lee; Synthia H. Mellon; Owen M. Wolkowitz; Susanne Mueller; Rachel Yehuda; Marti Jett; Charles R. Marmar; Kerry J. Ressler; Francis J. Doyle Polygenic Risk Associated with Post-Traumatic Stress Disorder Onset and Severity Journal Article Translational Psychiatry, 9 (1), pp. 1-8, 2019, ISSN: 2158-3188. @article{misganawPolygenicRiskAssociated2019, title = {Polygenic Risk Associated with Post-Traumatic Stress Disorder Onset and Severity}, author = { Burook Misganaw and Guia Guffanti and Adriana Lori and Duna {Abu-Amara} and Janine D. Flory and Rasha Hammamieh and Aarti Gautam and Ruoting Yang and Bernie J. Daigle Jr. and Leroy Hood and Kai Wang and Inyoul Lee and Synthia H. Mellon and Owen M. Wolkowitz and Susanne Mueller and Rachel Yehuda and Marti Jett and Charles R. Marmar and Kerry J. Ressler and Francis J. Doyle}, doi = {10.1038/s41398-019-0497-3}, issn = {2158-3188}, year = {2019}, date = {2019-06-07}, journal = {Translational Psychiatry}, volume = {9}, number = {1}, pages = {1-8}, abstract = {Post-traumatic stress disorder (PTSD) is a psychiatric illness with a highly polygenic architecture without large effect-size common single-nucleotide polymorphisms (SNPs). Thus, to capture a substantial portion of the genetic contribution, effects from many variants need to be aggregated. We investigated various aspects of one such approach that has been successfully applied to many traits, polygenic risk score (PRS) for PTSD. Theoretical analyses indicate the potential prediction ability of PRS. We used the latest summary statistics from the largest published genome-wide association study (GWAS) conducted by Psychiatric Genomics Consortium for PTSD (PGC-PTSD). We found that the PRS constructed for a cohort comprising veterans of recent wars (n,=,244) explains a considerable proportion of PTSD onset (Nagelkerke R2,=,4.68%}, keywords = {}, pubstate = {published}, tppubtype = {article} } Post-traumatic stress disorder (PTSD) is a psychiatric illness with a highly polygenic architecture without large effect-size common single-nucleotide polymorphisms (SNPs). Thus, to capture a substantial portion of the genetic contribution, effects from many variants need to be aggregated. We investigated various aspects of one such approach that has been successfully applied to many traits, polygenic risk score (PRS) for PTSD. Theoretical analyses indicate the potential prediction ability of PRS. We used the latest summary statistics from the largest published genome-wide association study (GWAS) conducted by Psychiatric Genomics Consortium for PTSD (PGC-PTSD). We found that the PRS constructed for a cohort comprising veterans of recent wars (n,=,244) explains a considerable proportion of PTSD onset (Nagelkerke R2,=,4.68% |
Rasha Hammamieh; Aarti Gautam; Nabarun Chakraborty; Seid Muhie; Ruoting Yang; Duncan Donohue; Bernie J. Daigle Jr.; Yuanyang Zhang; Duna Abu Amara; Janine Flory; Rachel Yehuda; Linda Petzhold; Frank Doyle; Charles Marmar; Marti Jett 229. A Cohort Study of OIF/OEF Veterans: A Blood Epigenomic Assessment Journal Article Biological Psychiatry, 85 (10), pp. S95, 2019, ISSN: 0006-3223, 1873-2402. @article{hammamieh229CohortStudy2019, title = {229. A Cohort Study of OIF/OEF Veterans: A Blood Epigenomic Assessment}, author = { Rasha Hammamieh and Aarti Gautam and Nabarun Chakraborty and Seid Muhie and Ruoting Yang and Duncan Donohue and Bernie J. Daigle Jr. and Yuanyang Zhang and Duna Abu Amara and Janine Flory and Rachel Yehuda and Linda Petzhold and Frank Doyle and Charles Marmar and Marti Jett}, doi = {10.1016/j.biopsych.2019.03.243}, issn = {0006-3223, 1873-2402}, year = {2019}, date = {2019-05-15}, journal = {Biological Psychiatry}, volume = {85}, number = {10}, pages = {S95}, publisher = {Elsevier}, abstract = {Management of post-traumatic stress disorder (PTSD) is complicated by the overlapping symptoms of its comorbidities. A comprehensive understanding of molecular pathophysiology of PTSD could facilitate unbiased biomarker-driven next-generation intervention strategies. In this study, epigenomic profiles were characterized as to the implications for behavior, immune response, nervous system development, and relevant PTSD comorbidities such as cardiac health and diabetes.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Management of post-traumatic stress disorder (PTSD) is complicated by the overlapping symptoms of its comorbidities. A comprehensive understanding of molecular pathophysiology of PTSD could facilitate unbiased biomarker-driven next-generation intervention strategies. In this study, epigenomic profiles were characterized as to the implications for behavior, immune response, nervous system development, and relevant PTSD comorbidities such as cardiac health and diabetes. |
2018 |
Bernie J. Daigle Jr. Bayesian Parameter Estimation and Markov Chain Monte Carlo Incollection Quantitative Biology: Theory, Computational Methods, and Models, pp. 339-355, MIT Press, 2018. @incollection{daiglejr.BayesianParameterEstimation2018, title = {Bayesian Parameter Estimation and Markov Chain Monte Carlo}, author = { Bernie J. Daigle Jr.}, year = {2018}, date = {2018-01-01}, booktitle = {Quantitative Biology: Theory, Computational Methods, and Models}, pages = {339-355}, publisher = {MIT Press}, keywords = {}, pubstate = {published}, tppubtype = {incollection} } |
2017 |
Eric C Rouchka; Julia H Chariker; David A Tieri; Juw Won Park; Shreedharkumar Rajurkar; Vikas Singh; Nishchal K Verma; Yan Cui; Mark Farman; Bradford Condon; Neil Moore; Jerzy Jaromczyk; Jolanta Jaromczyk; Daniel Harris; Patrick Calie; Eun Kyong Shin; Robert L Davis; Arash Shaban-Nejad; Joshua M Mitchell; Robert M Flight; Qing Jun Wang; Richard M Higashi; Teresa W-M Fan; Andrew N Lane; Hunter N B Moseley; Liangqun Lu; Bernie J Daigle Jr.; Xi Chen; Andrey Smelter; Hunter N B Moseley; Jerzy W Jaromczyk; Mark Farman; Li Chen; Neil Moore; Bailey K Phan; Nathaniel J Serpico; Ethan G Toney; Caroline E Melton; Jennifer R Mandel; Bernie J Daigle Jr.; Hao Chen; Kazi I Zaman; Ramin Homayouni; Patrick J Trainor; Samantha M Carlisle; Andrew P DeFilippis; Shesh N Rai Proceedings of the 16th Annual UT-KBRIN Bioinformatics Summit 2016: Bioinformatics Journal Article BMC Bioinformatics, 18 (9), pp. 377, 2017, ISSN: 1471-2105. @article{rouchka_proceedings_2017, title = {Proceedings of the 16th Annual UT-KBRIN Bioinformatics Summit 2016: Bioinformatics}, author = {Eric C Rouchka and Julia H Chariker and David A Tieri and Juw Won Park and Shreedharkumar Rajurkar and Vikas Singh and Nishchal K Verma and Yan Cui and Mark Farman and Bradford Condon and Neil Moore and Jerzy Jaromczyk and Jolanta Jaromczyk and Daniel Harris and Patrick Calie and Eun Kyong Shin and Robert L Davis and Arash Shaban-Nejad and Joshua M Mitchell and Robert M Flight and Qing Jun Wang and Richard M Higashi and Teresa W-M Fan and Andrew N Lane and Hunter N B Moseley and Liangqun Lu and Bernie J Daigle Jr. and Xi Chen and Andrey Smelter and Hunter N B Moseley and Jerzy W Jaromczyk and Mark Farman and Li Chen and Neil Moore and Bailey K Phan and Nathaniel J Serpico and Ethan G Toney and Caroline E Melton and Jennifer R Mandel and Bernie J Daigle Jr. and Hao Chen and Kazi I Zaman and Ramin Homayouni and Patrick J Trainor and Samantha M Carlisle and Andrew P DeFilippis and Shesh N Rai}, doi = {10.1186/s12859-017-1781-y}, issn = {1471-2105}, year = {2017}, date = {2017-10-13}, journal = {BMC Bioinformatics}, volume = {18}, number = {9}, pages = {377}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Rasha Hammamieh; Nabarun Chakraborty; Aarti Gautam; Seid Muhie; Ruoting Yang; Duncan Donohue; Raina Kumar; Bernie J Daigle Jr.; Yuanyang Zhang; Duna Abu-Amara; Stacy-Ann Miller; Seshmalini Srinivasan; Janine Flory; Rachel Yehuda; Linda Petzold; Owen M Wolkowitz; Synthia H Mellon; Leroy Hood; Francis J Doyle III; Charles Marmar; Marti Jett Whole-Genome DNA Methylation Status Associated with Clinical PTSD Measures of OIF/OEF Veterans Journal Article Translational Psychiatry, 7 (7), pp. e1169, 2017. @article{hammamieh_whole-genome_2017, title = {Whole-Genome DNA Methylation Status Associated with Clinical PTSD Measures of OIF/OEF Veterans}, author = {Rasha Hammamieh and Nabarun Chakraborty and Aarti Gautam and Seid Muhie and Ruoting Yang and Duncan Donohue and Raina Kumar and Bernie J Daigle Jr. and Yuanyang Zhang and Duna Abu-Amara and Stacy-Ann Miller and Seshmalini Srinivasan and Janine Flory and Rachel Yehuda and Linda Petzold and Owen M Wolkowitz and Synthia H Mellon and Leroy Hood and Francis J Doyle III and Charles Marmar and Marti Jett}, doi = {10.1038/tp.2017.129}, year = {2017}, date = {2017-07-11}, urldate = {2017-09-19}, journal = {Translational Psychiatry}, volume = {7}, number = {7}, pages = {e1169}, abstract = {Emerging knowledge suggests that post-traumatic stress disorder (PTSD) pathophysiology is linked to the patients' epigenetic changes, but comprehensive studies examining genome-wide methylation have not been performed. In this study, we examined genome-wide DNA methylation in peripheral whole blood in combat veterans with and without PTSD to ascertain differentially methylated probes. Discovery was initially made in a training sample comprising 48 male Operation Enduring Freedom (OEF)/Operation Iraqi Freedom (OIF) veterans with PTSD and 51 age/ethnicity/gender-matched combat-exposed PTSD-negative controls. Agilent whole-genome array detected textasciitilde5600 differentially methylated CpG islands (CpGI) annotated to textasciitilde2800 differently methylated genes (DMGs). The majority (84.5%) of these CpGIs were hypermethylated in the PTSD cases. Functional analysis was performed using the DMGs encoding the promoter-bound CpGIs to identify networks related to PTSD. The identified networks were further validated by an independent test set comprising 31 PTSD+/29 PTSD- veterans. Targeted bisulfite sequencing was also used to confirm the methylation status of 20 DMGs shown to be highly perturbed in the training set. To improve the statistical power and mitigate the assay bias and batch effects, a union set combining both training and test set was assayed using a different platform from Illumina. The pathways curated from this analysis confirmed 65% of the pool of pathways mined from training and test sets. The results highlight the importance of assay methodology and use of independent samples for discovery and validation of differentially methylated genes mined from whole blood. Nonetheless, the current study demonstrates that several important epigenetically altered networks may distinguish combat-exposed veterans with and without PTSD.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Emerging knowledge suggests that post-traumatic stress disorder (PTSD) pathophysiology is linked to the patients' epigenetic changes, but comprehensive studies examining genome-wide methylation have not been performed. In this study, we examined genome-wide DNA methylation in peripheral whole blood in combat veterans with and without PTSD to ascertain differentially methylated probes. Discovery was initially made in a training sample comprising 48 male Operation Enduring Freedom (OEF)/Operation Iraqi Freedom (OIF) veterans with PTSD and 51 age/ethnicity/gender-matched combat-exposed PTSD-negative controls. Agilent whole-genome array detected textasciitilde5600 differentially methylated CpG islands (CpGI) annotated to textasciitilde2800 differently methylated genes (DMGs). The majority (84.5%) of these CpGIs were hypermethylated in the PTSD cases. Functional analysis was performed using the DMGs encoding the promoter-bound CpGIs to identify networks related to PTSD. The identified networks were further validated by an independent test set comprising 31 PTSD+/29 PTSD- veterans. Targeted bisulfite sequencing was also used to confirm the methylation status of 20 DMGs shown to be highly perturbed in the training set. To improve the statistical power and mitigate the assay bias and batch effects, a union set combining both training and test set was assayed using a different platform from Illumina. The pathways curated from this analysis confirmed 65% of the pool of pathways mined from training and test sets. The results highlight the importance of assay methodology and use of independent samples for discovery and validation of differentially methylated genes mined from whole blood. Nonetheless, the current study demonstrates that several important epigenetically altered networks may distinguish combat-exposed veterans with and without PTSD. |
2016 |
Brian Drawert; Andreas Hellander; Ben Bales; Debjani Banerjee; Giovanni Bellesia; Bernie J Daigle Jr.; Geoffrey Douglas; Mengyuan Gu; Anand Gupta; Stefan Hellander; Chris Horuk; Dibyendu Nath; Aviral Takkar; Sheng Wu; Per Lötstedt; Chandra Krintz; Linda R Petzold Stochastic Simulation Service: Bridging the Gap between the Computational Expert and the Biologist Journal Article PLOS Computational Biology, 12 (12), pp. e1005220, 2016, ISSN: 1553-7358. @article{drawert_stochastic_2016, title = {Stochastic Simulation Service: Bridging the Gap between the Computational Expert and the Biologist}, author = {Brian Drawert and Andreas Hellander and Ben Bales and Debjani Banerjee and Giovanni Bellesia and Bernie J Daigle Jr. and Geoffrey Douglas and Mengyuan Gu and Anand Gupta and Stefan Hellander and Chris Horuk and Dibyendu Nath and Aviral Takkar and Sheng Wu and Per Lötstedt and Chandra Krintz and Linda R Petzold}, doi = {10.1371/journal.pcbi.1005220}, issn = {1553-7358}, year = {2016}, date = {2016-12-01}, urldate = {2016-12-10}, journal = {PLOS Computational Biology}, volume = {12}, number = {12}, pages = {e1005220}, abstract = {We present StochSS: Stochastic Simulation as a Service, an integrated development environment for modeling and simulation of both deterministic and discrete stochastic biochemical systems in up to three dimensions. An easy to use graphical user interface enables researchers to quickly develop and simulate a biological model on a desktop or laptop, which can then be expanded to incorporate increasing levels of complexity. StochSS features state-of-the-art simulation engines. As the demand for computational power increases, StochSS can seamlessly scale computing resources in the cloud. In addition, StochSS can be deployed as a multi-user software environment where collaborators share computational resources and exchange models via a public model repository. We demonstrate the capabilities and ease of use of StochSS with an example of model development and simulation at increasing levels of complexity.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We present StochSS: Stochastic Simulation as a Service, an integrated development environment for modeling and simulation of both deterministic and discrete stochastic biochemical systems in up to three dimensions. An easy to use graphical user interface enables researchers to quickly develop and simulate a biological model on a desktop or laptop, which can then be expanded to incorporate increasing levels of complexity. StochSS features state-of-the-art simulation engines. As the demand for computational power increases, StochSS can seamlessly scale computing resources in the cloud. In addition, StochSS can be deployed as a multi-user software environment where collaborators share computational resources and exchange models via a public model repository. We demonstrate the capabilities and ease of use of StochSS with an example of model development and simulation at increasing levels of complexity. |
Min K Roh; Bernie J Daigle Jr. SParSE++: Improved Event-Based Stochastic Parameter Search Journal Article BMC Systems Biology, 10 , pp. 109, 2016, ISSN: 1752-0509. @article{roh_sparse++:_2016, title = {SParSE++: Improved Event-Based Stochastic Parameter Search}, author = {Min K Roh and Bernie J Daigle Jr.}, doi = {10.1186/s12918-016-0367-z}, issn = {1752-0509}, year = {2016}, date = {2016-11-01}, urldate = {2016-11-30}, journal = {BMC Systems Biology}, volume = {10}, pages = {109}, abstract = {Despite the increasing availability of high performance computing capabilities, analysis and characterization of stochastic biochemical systems remain a computational challenge. To address this challenge, the Stochastic Parameter Search for Events (SParSE) was developed to automatically identify reaction rates that yield a probabilistic user-specified event. SParSE consists of three main components: the multi-level cross-entropy method, which identifies biasing parameters to push the system toward the event of interest, the related inverse biasing method, and an optional interpolation of identified parameters. While effective for many examples, SParSE depends on the existence of a sufficient amount of intrinsic stochasticity in the system of interest. In the absence of this stochasticity, SParSE can either converge slowly or not at all.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Despite the increasing availability of high performance computing capabilities, analysis and characterization of stochastic biochemical systems remain a computational challenge. To address this challenge, the Stochastic Parameter Search for Events (SParSE) was developed to automatically identify reaction rates that yield a probabilistic user-specified event. SParSE consists of three main components: the multi-level cross-entropy method, which identifies biasing parameters to push the system toward the event of interest, the related inverse biasing method, and an optional interpolation of identified parameters. While effective for many examples, SParSE depends on the existence of a sufficient amount of intrinsic stochasticity in the system of interest. In the absence of this stochasticity, SParSE can either converge slowly or not at all. |
Eric C Rouchka; Julia H Chariker; Benjamin J Harrison; Juw Won Park; Xueyuan Cao; Stanley Pounds; Susana Raimondi; James Downing; Raul Ribeiro; Jeffery Rubnitz; Jatinder Lamba; Bernie J Daigle Jr.; Deborah Burgess; Stephanie Gehrlich; John C Carmen; Nicholas Johnson; Chandrakanth Emani; Stephanie Gehrlich; Deborah Burgess; John C Carmen; Kalpani De Silva; Michael P Heaton; Theodore S Kalbfleisch; Teeradache Viangteeravat; Rahul Mudunuri; Oluwaseun Ajayi; Fatih c Sen; Eunice Y Huang; Mohammad Mohebbi; Luaire Florian; Douglas J Jackson; John F Naber; AKM Sabbir; Sally R Ellingson; Yuping Lu; Charles A Phillips; Michael A Langston; Rahul K Sevakula; Raghuveer Thirukovalluru; Nishchal K Verma; Yan Cui; Mohammed Sayed; Juw Won Park; Jing Wang; Qi Liu; Yu Shyr; Xiaofei Zhang; Sally R Ellingson; Naresh Prodduturi; Gavin R Oliver; Diane Grill; Jie Na; Jeanette Eckel-Passow; Eric W Klee; Michael M Goodin; Mark Farman; Harrison Inocencio; Chanyong Jang; Jerzy W Jaromczyk; Neil Moore; Kelly Sovacool; Leon Dent; Mike Izban; Sammed Mandape; Shruti Sakhare; Siddharth Pratap; Dana Marshall; Scotty M DePriest; James N MacLeod; Theodore S Kalbfleisch; Chandrakanth Emani; Hanady Adam; Ethan Blandford; Joel Campbell; Joshua Castlen; Brittany Dixon; Ginger Gilbert; Aaron Hall; Philip Kreisle; Jessica Lasher; Bethany Oakes; Allison Speer; Maximilian Valentine; Naga Satya Rao V Nagisetty; Rony Jose; Teeradache Viangteeravat; Robert Rooney; David Hains Proceedings of the 15th Annual UT-KBRIN Bioinformatics Summit 2016 Journal Article BMC Bioinformatics, 17 (10), pp. 297, 2016, ISSN: 1471-2105. @article{rouchka_proceedings_2016, title = {Proceedings of the 15th Annual UT-KBRIN Bioinformatics Summit 2016}, author = {Eric C Rouchka and Julia H Chariker and Benjamin J Harrison and Juw Won Park and Xueyuan Cao and Stanley Pounds and Susana Raimondi and James Downing and Raul Ribeiro and Jeffery Rubnitz and Jatinder Lamba and Bernie J Daigle Jr. and Deborah Burgess and Stephanie Gehrlich and John C Carmen and Nicholas Johnson and Chandrakanth Emani and Stephanie Gehrlich and Deborah Burgess and John C Carmen and Kalpani De Silva and Michael P Heaton and Theodore S Kalbfleisch and Teeradache Viangteeravat and Rahul Mudunuri and Oluwaseun Ajayi and Fatih {c S}en and Eunice Y Huang and Mohammad Mohebbi and Luaire Florian and Douglas J Jackson and John F Naber and AKM Sabbir and Sally R Ellingson and Yuping Lu and Charles A Phillips and Michael A Langston and Rahul K Sevakula and Raghuveer Thirukovalluru and Nishchal K Verma and Yan Cui and Mohammed Sayed and Juw Won Park and Jing Wang and Qi Liu and Yu Shyr and Xiaofei Zhang and Sally R Ellingson and Naresh Prodduturi and Gavin R Oliver and Diane Grill and Jie Na and Jeanette Eckel-Passow and Eric W Klee and Michael M Goodin and Mark Farman and Harrison Inocencio and Chanyong Jang and Jerzy W Jaromczyk and Neil Moore and Kelly Sovacool and Leon Dent and Mike Izban and Sammed Mandape and Shruti Sakhare and Siddharth Pratap and Dana Marshall and Scotty M DePriest and James N MacLeod and Theodore S Kalbfleisch and Chandrakanth Emani and Hanady Adam and Ethan Blandford and Joel Campbell and Joshua Castlen and Brittany Dixon and Ginger Gilbert and Aaron Hall and Philip Kreisle and Jessica Lasher and Bethany Oakes and Allison Speer and Maximilian Valentine and Naga Satya Rao V Nagisetty and Rony Jose and Teeradache Viangteeravat and Robert Rooney and David Hains}, doi = {10.1186/s12859-016-1154-y}, issn = {1471-2105}, year = {2016}, date = {2016-08-01}, urldate = {2016-11-30}, journal = {BMC Bioinformatics}, volume = {17}, number = {10}, pages = {297}, abstract = {I1 Proceedings of the Fifteenth Annual UT- KBRIN Bioinformatics Summit 2016}, keywords = {}, pubstate = {published}, tppubtype = {article} } I1 Proceedings of the Fifteenth Annual UT- KBRIN Bioinformatics Summit 2016 |
Nabarun Chakraborty; Seid Muhie; Ruoting Yang; Aarti Gautam; Duncan Donohue; Bernie J Daigle Jr.; Yuanyang Zhang; Duna Abu Amara; Jannie Flory; Rachel Yehuda; Frank Doyle; Rasha Hammamieh; Charles Marmar; Marti Jett Characterization of the Epigenomic Status of the US OEF/OIF War Veterans: A Pilot Clinical Study Journal Article The FASEB Journal, 30 (1 Supplement), pp. 831.3–831.3, 2016, ISSN: 0892-6638, 1530-6860. @article{chakraborty_characterization_2016, title = {Characterization of the Epigenomic Status of the US OEF/OIF War Veterans: A Pilot Clinical Study}, author = {Nabarun Chakraborty and Seid Muhie and Ruoting Yang and Aarti Gautam and Duncan Donohue and Bernie J Daigle Jr. and Yuanyang Zhang and Duna Abu Amara and Jannie Flory and Rachel Yehuda and Frank Doyle and Rasha Hammamieh and Charles Marmar and Marti Jett}, issn = {0892-6638, 1530-6860}, year = {2016}, date = {2016-01-01}, urldate = {2016-07-28}, journal = {The FASEB Journal}, volume = {30}, number = {1 Supplement}, pages = {831.3--831.3}, abstract = {Management of post-traumatic stress disorder (PTSD) is complicated by the overlapping symptoms of its co morbidities and the diagnostic reliance on self-report and time consuming psychological evaluation. A more comprehensive understanding of molecular pathophysiology of PTSD could facilitate an unbiased biomarker-driven next-generation intervention strategy. Herein, we cast light on the epigenomic consequences of combat elicited PTSD. In this study, hypermethylated genes were investigated as to the implications for behavior, immune response, nervous system development, and relevant PTSD co-morbidities such as cardiac health and diabetes. 52 PTSD-positive male veterans of US Operation Iraqi Freedom (OIF) and Operation Enduring Freedom (OEF) were matched to 52 controls by age and ethnicity. PTSD diagnosis was determined by a clinician-administered PTSD scale (CAPS), score $>$40, while the control group demonstrated a CAPS $<$10. Methylation status of DNA extracted from whole blood was assayed using high density arrays (Agilent, Inc.). 5,000 probes were statistically differentially methylated (FDR $<$ 0.1), representing approximately 3,600 unique genes. Chromosome 4 and 18 imprints a significantly large portion of the methylated probes, including those which control emotional and cognition process, and glucocorticoid deficiency. Interestingly, a significant number of genes facilitating telomere maintenance and insulin reception were hypermethylated at both promoter and gene body sites; therefore the DNA methylation status in these genes could be prevailing. Nearly 85% of the differentially methylatedprobes were hypermethylated in PTSD patients. The majority of these probes encode the candidate proteins responsible for transcription regulation and enzymaticactions. Genes involved in memory consolidation, emotion/aggressive behavior, and perturbed circadian rhythm were preferentially hypermethylated. PTSD epigenetically perturbed both the cellular and humoral immune system; in addition the morphologies of two brain regions known to control PTSD symptoms, namely cerebral cortex and hippocampus were perturbed. Genes involved in several PTSD comorbidities, such as cardiomyopathy and poor insulin management, were also hypermethylated. Integration of the epigenomic observations with other omics outcomes is underway, as well as validation of these findings in an independent cohort.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Management of post-traumatic stress disorder (PTSD) is complicated by the overlapping symptoms of its co morbidities and the diagnostic reliance on self-report and time consuming psychological evaluation. A more comprehensive understanding of molecular pathophysiology of PTSD could facilitate an unbiased biomarker-driven next-generation intervention strategy. Herein, we cast light on the epigenomic consequences of combat elicited PTSD. In this study, hypermethylated genes were investigated as to the implications for behavior, immune response, nervous system development, and relevant PTSD co-morbidities such as cardiac health and diabetes. 52 PTSD-positive male veterans of US Operation Iraqi Freedom (OIF) and Operation Enduring Freedom (OEF) were matched to 52 controls by age and ethnicity. PTSD diagnosis was determined by a clinician-administered PTSD scale (CAPS), score $>$40, while the control group demonstrated a CAPS $<$10. Methylation status of DNA extracted from whole blood was assayed using high density arrays (Agilent, Inc.). 5,000 probes were statistically differentially methylated (FDR $<$ 0.1), representing approximately 3,600 unique genes. Chromosome 4 and 18 imprints a significantly large portion of the methylated probes, including those which control emotional and cognition process, and glucocorticoid deficiency. Interestingly, a significant number of genes facilitating telomere maintenance and insulin reception were hypermethylated at both promoter and gene body sites; therefore the DNA methylation status in these genes could be prevailing. Nearly 85% of the differentially methylatedprobes were hypermethylated in PTSD patients. The majority of these probes encode the candidate proteins responsible for transcription regulation and enzymaticactions. Genes involved in memory consolidation, emotion/aggressive behavior, and perturbed circadian rhythm were preferentially hypermethylated. PTSD epigenetically perturbed both the cellular and humoral immune system; in addition the morphologies of two brain regions known to control PTSD symptoms, namely cerebral cortex and hippocampus were perturbed. Genes involved in several PTSD comorbidities, such as cardiomyopathy and poor insulin management, were also hypermethylated. Integration of the epigenomic observations with other omics outcomes is underway, as well as validation of these findings in an independent cohort. |
Yuanyang Zhang; Tie Bo Wu; Bernie J Daigle Jr.; Mitchell Cohen; Linda Petzold Identification of Disease States Associated with Coagulopathy in Trauma Journal Article BMC Medical Informatics and Decision Making, 16 , pp. 124, 2016, ISSN: 1472-6947. @article{zhang_identification_2016, title = {Identification of Disease States Associated with Coagulopathy in Trauma}, author = {Yuanyang Zhang and Tie Bo Wu and Bernie J Daigle Jr. and Mitchell Cohen and Linda Petzold}, doi = {10.1186/s12911-016-0360-x}, issn = {1472-6947}, year = {2016}, date = {2016-01-01}, urldate = {2016-09-23}, journal = {BMC Medical Informatics and Decision Making}, volume = {16}, pages = {124}, abstract = {Trauma is the leading cause of death between the ages of 1 to 44 in the United States. Blood loss is the primary cause of these deaths. The discrimination of states through which patients transition would be helpful in understanding the disease process, and in identification of critical states and appropriate interventions. Even though these states are strongly associated with patients' blood composition data, there has not been a way to directly identify them. Statistical tools such as hidden Markov models can be used to infer the discrete latent states from the blood composition data.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Trauma is the leading cause of death between the ages of 1 to 44 in the United States. Blood loss is the primary cause of these deaths. The discrimination of states through which patients transition would be helpful in understanding the disease process, and in identification of critical states and appropriate interventions. Even though these states are strongly associated with patients' blood composition data, there has not been a way to directly identify them. Statistical tools such as hidden Markov models can be used to infer the discrete latent states from the blood composition data. |
Gunjan S Thakur; Bernie J Daigle Jr.; Meng Qian; Kelsey R Dean; Yuanyang Zhang; Ruoting Yang; Taek-Kyun Kim; Xiaogang Wu; Meng Li; Inyoul Lee; Linda R Petzold; Francis J Doyle III A Multi-Metric Evaluation of Stratified Random Sampling for Classification: A Case Study Journal Article IEEE Life Sciences Letters, PP (99), pp. 1–1, 2016. @article{thakur_multi-metric_2016, title = {A Multi-Metric Evaluation of Stratified Random Sampling for Classification: A Case Study}, author = {Gunjan S Thakur and Bernie J Daigle Jr. and Meng Qian and Kelsey R Dean and Yuanyang Zhang and Ruoting Yang and Taek-Kyun Kim and Xiaogang Wu and Meng Li and Inyoul Lee and Linda R Petzold and Francis J Doyle III}, doi = {10.1109/LLS.2016.2615086}, year = {2016}, date = {2016-01-01}, journal = {IEEE Life Sciences Letters}, volume = {PP}, number = {99}, pages = {1--1}, abstract = {Accurate classification of biological phenotypes is an essential task for medical decision making. The selection of subjects for classifier training and validation sets is a crucial step within this task. To evaluate the impact of two approaches for subject selection–randomization and clinical balancing, we applied six classification algorithms to a highly replicated publicly available breast cancer dataset. Using six performance metrics, we demonstrate that clinical balancing improves both training and validation performance for all methods on average. We also observed a smaller discrepancy between training and validation performance. Further a simple analytical argument is presented which suggests that we need only two metrics from the class of metrics based on the entries of the confusion matrix. In light of our results, we recommend (1) clinical balancing of training and validation data to improve signal to noise ratio and (2) the use of multiple classification algorithms and evaluation metrics for a comprehensive evaluation of the decision making process.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Accurate classification of biological phenotypes is an essential task for medical decision making. The selection of subjects for classifier training and validation sets is a crucial step within this task. To evaluate the impact of two approaches for subject selection–randomization and clinical balancing, we applied six classification algorithms to a highly replicated publicly available breast cancer dataset. Using six performance metrics, we demonstrate that clinical balancing improves both training and validation performance for all methods on average. We also observed a smaller discrepancy between training and validation performance. Further a simple analytical argument is presented which suggests that we need only two metrics from the class of metrics based on the entries of the confusion matrix. In light of our results, we recommend (1) clinical balancing of training and validation data to improve signal to noise ratio and (2) the use of multiple classification algorithms and evaluation metrics for a comprehensive evaluation of the decision making process. |
2015 |
Gunjan S Thakur; Bernie J Daigle Jr.; Kelsey Dean; Linda R Petzold; Francis J Doyle III Metric Focused Feature Selection for Customized Biomarker Identification Inproceedings The Fifth International Conference on Foundations of Systems Biology in Engineering (FOSBE 2015), 2015. @inproceedings{Thakur:2015aa, title = {Metric Focused Feature Selection for Customized Biomarker Identification}, author = {Gunjan S Thakur and Bernie J Daigle Jr. and Kelsey Dean and Linda R Petzold and Francis J Doyle III}, year = {2015}, date = {2015-08-01}, booktitle = {The Fifth International Conference on Foundations of Systems Biology in Engineering (FOSBE 2015)}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Gunjan S Thakur; Bernie J Daigle Jr.; Kelsey R Dean; Yuanyang Zhang; Maria Rodriguez-Fernandez; Rasha Hammamieh; Ruoting Yang; Marti Jett; Joseph Palma; Linda R Petzold; Francis J Doyle III Systems Biology Approach to Understanding Post-Traumatic Stress Disorder Journal Article Molecular BioSystems, 11 (4), pp. 980–993, 2015, ISSN: 1742-2051. @article{thakur_systems_2015, title = {Systems Biology Approach to Understanding Post-Traumatic Stress Disorder}, author = {Gunjan S Thakur and Bernie J Daigle Jr. and Kelsey R Dean and Yuanyang Zhang and Maria Rodriguez-Fernandez and Rasha Hammamieh and Ruoting Yang and Marti Jett and Joseph Palma and Linda R Petzold and Francis J Doyle III}, doi = {10.1039/C4MB00404C}, issn = {1742-2051}, year = {2015}, date = {2015-03-01}, urldate = {2015-10-16}, journal = {Molecular BioSystems}, volume = {11}, number = {4}, pages = {980--993}, abstract = {Post-traumatic stress disorder (PTSD) is a psychological disorder affecting individuals that have experienced life-changing traumatic events. The symptoms of PTSD experienced by these subjects—including acute anxiety, flashbacks, and hyper-arousal—disrupt their normal functioning. Although PTSD is still categorized as a psychological disorder, recent years have witnessed a multi-directional research effort attempting to understand the biomolecular origins of the disorder. This review begins by providing a brief overview of the known biological underpinnings of the disorder resulting from studies using structural and functional neuroimaging, endocrinology, and genetic and epigenetic assays. Next, we discuss the systems biology approach, which is often used to gain mechanistic insights from the wealth of available high-throughput experimental data. Finally, we provide an overview of the current computational tools used to decipher the heterogeneous types of molecular data collected in the study of PTSD.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Post-traumatic stress disorder (PTSD) is a psychological disorder affecting individuals that have experienced life-changing traumatic events. The symptoms of PTSD experienced by these subjects—including acute anxiety, flashbacks, and hyper-arousal—disrupt their normal functioning. Although PTSD is still categorized as a psychological disorder, recent years have witnessed a multi-directional research effort attempting to understand the biomolecular origins of the disorder. This review begins by providing a brief overview of the known biological underpinnings of the disorder resulting from studies using structural and functional neuroimaging, endocrinology, and genetic and epigenetic assays. Next, we discuss the systems biology approach, which is often used to gain mechanistic insights from the wealth of available high-throughput experimental data. Finally, we provide an overview of the current computational tools used to decipher the heterogeneous types of molecular data collected in the study of PTSD. |
Bernie J Daigle Jr.; Mohammad Soltani; Linda R Petzold; Abhyudai Singh Inferring Single-Cell Gene Expression Mechanisms Using Stochastic Simulation Journal Article Bioinformatics, 31 (9), pp. 1428–1435, 2015, ISSN: 1367-4803, 1460-2059. @article{daigle_jr._inferring_2015, title = {Inferring Single-Cell Gene Expression Mechanisms Using Stochastic Simulation}, author = {Bernie J Daigle Jr. and Mohammad Soltani and Linda R Petzold and Abhyudai Singh}, doi = {10.1093/bioinformatics/btv007}, issn = {1367-4803, 1460-2059}, year = {2015}, date = {2015-01-01}, urldate = {2015-10-16}, journal = {Bioinformatics}, volume = {31}, number = {9}, pages = {1428--1435}, abstract = {Motivation: Stochastic promoter switching between transcriptionally active (ON) and inactive (OFF) states is a major source of noise in gene expression. It is often implicitly assumed that transitions between promoter states are memoryless, i.e. promoters spend an exponentially distributed time interval in each of the two states. However, increasing evidence suggests that promoter ON/OFF times can be non-exponential, hinting at more complex transcriptional regulatory architectures. Given the essential role of gene expression in all cellular functions, efficient computational techniques for characterizing promoter architectures are critically needed. Results: We have developed a novel model reduction for promoters with arbitrary numbers of ON and OFF states, allowing us to approximate complex promoter switching behavior with Weibull-distributed ON/OFF times. Using this model reduction, we created bursty Monte Carlo expectation-maximization with modified cross-entropy method (`bursty MCEM2'), an efficient parameter estimation and model selection technique for inferring the number and configuration of promoter states from single-cell gene expression data. Application of bursty MCEM2 to data from the endogenous mouse glutaminase promoter reveals nearly deterministic promoter OFF times, consistent with a multi-step activation mechanism consisting of 10 or more inactive states. Our novel approach to modeling promoter fluctuations together with bursty MCEM2 provides powerful tools for characterizing transcriptional bursting across genes under different environmental conditions. Availability and implementation: R source code implementing bursty MCEM2 is available upon request. Contact: absingh@udel.edu Supplementary information: Supplementary data are available at Bioinformatics online.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Motivation: Stochastic promoter switching between transcriptionally active (ON) and inactive (OFF) states is a major source of noise in gene expression. It is often implicitly assumed that transitions between promoter states are memoryless, i.e. promoters spend an exponentially distributed time interval in each of the two states. However, increasing evidence suggests that promoter ON/OFF times can be non-exponential, hinting at more complex transcriptional regulatory architectures. Given the essential role of gene expression in all cellular functions, efficient computational techniques for characterizing promoter architectures are critically needed. Results: We have developed a novel model reduction for promoters with arbitrary numbers of ON and OFF states, allowing us to approximate complex promoter switching behavior with Weibull-distributed ON/OFF times. Using this model reduction, we created bursty Monte Carlo expectation-maximization with modified cross-entropy method (`bursty MCEM2'), an efficient parameter estimation and model selection technique for inferring the number and configuration of promoter states from single-cell gene expression data. Application of bursty MCEM2 to data from the endogenous mouse glutaminase promoter reveals nearly deterministic promoter OFF times, consistent with a multi-step activation mechanism consisting of 10 or more inactive states. Our novel approach to modeling promoter fluctuations together with bursty MCEM2 provides powerful tools for characterizing transcriptional bursting across genes under different environmental conditions. Availability and implementation: R source code implementing bursty MCEM2 is available upon request. Contact: absingh@udel.edu Supplementary information: Supplementary data are available at Bioinformatics online. |
Yuanyang Zhang; Bernie J Daigle Jr.; Mitchell Cohen; Linda R Petzold A Cure Time Model for Joint Prediction of Outcome and Time-to-Outcome Inproceedings 2015 IEEE International Conference on Data Mining (ICDM), pp. 1117–1122, 2015. @inproceedings{zhang_cure_2015, title = {A Cure Time Model for Joint Prediction of Outcome and Time-to-Outcome}, author = {Yuanyang Zhang and Bernie J Daigle Jr. and Mitchell Cohen and Linda R Petzold}, doi = {10.1109/ICDM.2015.14}, year = {2015}, date = {2015-01-01}, booktitle = {2015 IEEE International Conference on Data Mining (ICDM)}, pages = {1117--1122}, abstract = {The Cox model has been widely used in time-to-outcome predictions, particularly in studies of medical patients, where prediction of the time of death is desired. In addition, the cure model has been proposed to model times of death for discharged patients. However, neither the Cox model nor the cure model allow explicit cure information and prediction of patient cure times (discharge times). In this paper we propose a new model, the "cure time model", which models the static data for dying patients, surviving patients, and their death/cure times jointly. It models (1) mortality via logistic regression and (2) death and discharge times via Cox models. We extend the cure time model to situations with censored data, where neither time of death nor discharge time are known, as well as to multiple ($>$2) outcomes. In addition, we propose a joint log-odds ratio which can predict the mortality of patients using the information from both the logistic regression and Cox models. We compare our model with the Cox and cure models on a trauma patient dataset from UCSF/San Francisco General Hospital. Our results show that the cure time model more accurately predicts both mortality and time-to-mortality for patients from these datasets.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } The Cox model has been widely used in time-to-outcome predictions, particularly in studies of medical patients, where prediction of the time of death is desired. In addition, the cure model has been proposed to model times of death for discharged patients. However, neither the Cox model nor the cure model allow explicit cure information and prediction of patient cure times (discharge times). In this paper we propose a new model, the "cure time model", which models the static data for dying patients, surviving patients, and their death/cure times jointly. It models (1) mortality via logistic regression and (2) death and discharge times via Cox models. We extend the cure time model to situations with censored data, where neither time of death nor discharge time are known, as well as to multiple ($>$2) outcomes. In addition, we propose a joint log-odds ratio which can predict the mortality of patients using the information from both the logistic regression and Cox models. We compare our model with the Cox and cure models on a trauma patient dataset from UCSF/San Francisco General Hospital. Our results show that the cure time model more accurately predicts both mortality and time-to-mortality for patients from these datasets. |
2014 |
Yuanyang Zhang; Bernie J Daigle Jr.; Lisa Ferrigno; Mitchell Cohen; Linda R Petzold Data-Driven Mortality Prediction for Trauma Patients Journal Article Selected for presentation at the Machine Learning in Computational Biology workshop (MLCB 2014) in the Twenty-eighth Annual Conference on Neural Information Processing Systems (NIPS 2014), 2014. @article{Zhang:2014aa, title = {Data-Driven Mortality Prediction for Trauma Patients}, author = {Yuanyang Zhang and Bernie J Daigle Jr. and Lisa Ferrigno and Mitchell Cohen and Linda R Petzold}, year = {2014}, date = {2014-12-01}, journal = {Selected for presentation at the Machine Learning in Computational Biology workshop (MLCB 2014) in the Twenty-eighth Annual Conference on Neural Information Processing Systems (NIPS 2014)}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Gunjan S Thakur; Bernie J Daigle Jr.; Linda R Petzold; Francis J Doyle III A Multivariate Ensemble Approach for Identification of Biomarkers: Application to Breast Cancer Inproceedings The 19th World Congress of the International Federation of Automatic Control (IFAC), pp. 809–814, 2014. @inproceedings{Thakur:2014aa, title = {A Multivariate Ensemble Approach for Identification of Biomarkers: Application to Breast Cancer}, author = {Gunjan S Thakur and Bernie J Daigle Jr. and Linda R Petzold and Francis J Doyle III}, year = {2014}, date = {2014-08-01}, booktitle = {The 19th World Congress of the International Federation of Automatic Control (IFAC)}, pages = {809--814}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
2013 |
Linda Petzold; Yuanyang Zhang; Bernie J Daigle Jr.; Lisa Ferrigno; Mitch Cohen Toward a Data-Driven Model of Trauma Dynamics Journal Article Journal of Critical Care, 28 (6), pp. e37, 2013, ISSN: 0883-9441. @article{petzold_toward_2013, title = {Toward a Data-Driven Model of Trauma Dynamics}, author = {Linda Petzold and Yuanyang Zhang and Bernie J Daigle Jr. and Lisa Ferrigno and Mitch Cohen}, doi = {10.1016/j.jcrc.2013.07.028}, issn = {0883-9441}, year = {2013}, date = {2013-12-01}, urldate = {2015-10-16}, journal = {Journal of Critical Care}, volume = {28}, number = {6}, pages = {e37}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Ruoting Yang; Bernie J Daigle Jr.; Seid Y Muhie; Rasha Hammamieh; Marti Jett; Linda Petzold; Francis J Doyle III Core Modular Blood and Brain Biomarkers in Social Defeat Mouse Model for Post Traumatic Stress Disorder Journal Article BMC Systems Biology, 7 (1), pp. 80, 2013, ISSN: 1752-0509. @article{yang_core_2013, title = {Core Modular Blood and Brain Biomarkers in Social Defeat Mouse Model for Post Traumatic Stress Disorder}, author = {Ruoting Yang and Bernie J Daigle Jr. and Seid Y Muhie and Rasha Hammamieh and Marti Jett and Linda Petzold and Francis J Doyle III}, doi = {10.1186/1752-0509-7-80}, issn = {1752-0509}, year = {2013}, date = {2013-01-01}, urldate = {2015-10-16}, journal = {BMC Systems Biology}, volume = {7}, number = {1}, pages = {80}, abstract = {Post-traumatic stress disorder (PTSD) is a severe anxiety disorder that affects a substantial portion of combat veterans and poses serious consequences to long-term health. Consequently, the identification of diagnostic and prognostic blood biomarkers for PTSD is of great interest. Previously, we assessed genome-wide gene expression of seven brain regions and whole blood in a social defeat mouse model subjected to various stress conditions.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Post-traumatic stress disorder (PTSD) is a severe anxiety disorder that affects a substantial portion of combat veterans and poses serious consequences to long-term health. Consequently, the identification of diagnostic and prognostic blood biomarkers for PTSD is of great interest. Previously, we assessed genome-wide gene expression of seven brain regions and whole blood in a social defeat mouse model subjected to various stress conditions. |
2012 |
Ruoting Yang; Bernie J Daigle Jr.; Linda R Petzold; Francis J Doyle III Core Module Biomarker Identification with Network Exploration for Breast Cancer Metastasis Journal Article BMC Bioinformatics, 13 (1), pp. 12, 2012, ISSN: 1471-2105. @article{yang_core_2012-1, title = {Core Module Biomarker Identification with Network Exploration for Breast Cancer Metastasis}, author = {Ruoting Yang and Bernie J Daigle Jr. and Linda R Petzold and Francis J Doyle III}, doi = {10.1186/1471-2105-13-12}, issn = {1471-2105}, year = {2012}, date = {2012-01-01}, urldate = {2015-10-16}, journal = {BMC Bioinformatics}, volume = {13}, number = {1}, pages = {12}, abstract = {In a complex disease, the expression of many genes can be significantly altered, leading to the appearance of a differentially expressed "disease module". Some of these genes directly correspond to the disease phenotype, (i.e. "driver" genes), while others represent closely-related first-degree neighbours in gene interaction space. The remaining genes consist of further removed "passenger" genes, which are often not directly related to the original cause of the disease. For prognostic and diagnostic purposes, it is crucial to be able to separate the group of "driver" genes and their first-degree neighbours, (i.e. "core module") from the general "disease module".}, keywords = {}, pubstate = {published}, tppubtype = {article} } In a complex disease, the expression of many genes can be significantly altered, leading to the appearance of a differentially expressed "disease module". Some of these genes directly correspond to the disease phenotype, (i.e. "driver" genes), while others represent closely-related first-degree neighbours in gene interaction space. The remaining genes consist of further removed "passenger" genes, which are often not directly related to the original cause of the disease. For prognostic and diagnostic purposes, it is crucial to be able to separate the group of "driver" genes and their first-degree neighbours, (i.e. "core module") from the general "disease module". |
Bernie J Daigle Jr.; Min K Roh; Linda R Petzold; Jarad Niemi Accelerated Maximum Likelihood Parameter Estimation for Stochastic Biochemical Systems Journal Article BMC Bioinformatics, 13 (1), pp. 68, 2012, ISSN: 1471-2105. @article{daigle_jr._accelerated_2012, title = {Accelerated Maximum Likelihood Parameter Estimation for Stochastic Biochemical Systems}, author = {Bernie J Daigle Jr. and Min K Roh and Linda R Petzold and Jarad Niemi}, doi = {10.1186/1471-2105-13-68}, issn = {1471-2105}, year = {2012}, date = {2012-01-01}, urldate = {2015-10-16}, journal = {BMC Bioinformatics}, volume = {13}, number = {1}, pages = {68}, abstract = {A prerequisite for the mechanistic simulation of a biochemical system is detailed knowledge of its kinetic parameters. Despite recent experimental advances, the estimation of unknown parameter values from observed data is still a bottleneck for obtaining accurate simulation results. Many methods exist for parameter estimation in deterministic biochemical systems; methods for discrete stochastic systems are less well developed. Given the probabilistic nature of stochastic biochemical models, a natural approach is to choose parameter values that maximize the probability of the observed data with respect to the unknown parameters, a.k.a. the maximum likelihood parameter estimates (MLEs). MLE computation for all but the simplest models requires the simulation of many system trajectories that are consistent with experimental data. For models with unknown parameters, this presents a computational challenge, as the generation of consistent trajectories can be an extremely rare occurrence.}, keywords = {}, pubstate = {published}, tppubtype = {article} } A prerequisite for the mechanistic simulation of a biochemical system is detailed knowledge of its kinetic parameters. Despite recent experimental advances, the estimation of unknown parameter values from observed data is still a bottleneck for obtaining accurate simulation results. Many methods exist for parameter estimation in deterministic biochemical systems; methods for discrete stochastic systems are less well developed. Given the probabilistic nature of stochastic biochemical models, a natural approach is to choose parameter values that maximize the probability of the observed data with respect to the unknown parameters, a.k.a. the maximum likelihood parameter estimates (MLEs). MLE computation for all but the simplest models requires the simulation of many system trajectories that are consistent with experimental data. For models with unknown parameters, this presents a computational challenge, as the generation of consistent trajectories can be an extremely rare occurrence. |
Robin E White; Curtis Palm; Lijun Xu; Evelyn Ling; Mitchell Ginsburg; Bernie J Daigle Jr.; Ruquan Han; Andrew Patterson; Russ B Altman; Rona G Giffard Mice Lacking the $B$2 Adrenergic Receptor Have a Unique Genetic Profile before and after Focal Brain Ischaemia Journal Article ASN Neuro, 4 (5), pp. AN20110020, 2012, ISSN: , 1759-0914. @article{white_mice_2012, title = {Mice Lacking the $B$2 Adrenergic Receptor Have a Unique Genetic Profile before and after Focal Brain Ischaemia}, author = {Robin E White and Curtis Palm and Lijun Xu and Evelyn Ling and Mitchell Ginsburg and Bernie J Daigle Jr. and Ruquan Han and Andrew Patterson and Russ B Altman and Rona G Giffard}, doi = {10.1042/AN20110020}, issn = {, 1759-0914}, year = {2012}, date = {2012-01-01}, urldate = {2015-10-16}, journal = {ASN Neuro}, volume = {4}, number = {5}, pages = {AN20110020}, abstract = {The role of the $beta$2AR ($beta$2 adrenergic receptor) after stroke is unclear as pharmacological manipulations of the $beta$2AR have produced contradictory results. We previously showed that mice deficient in the $beta$2AR ($beta$2KO) had smaller infarcts compared with WT (wild-type) mice (FVB) after MCAO (middle cerebral artery occlusion), a model of stroke. To elucidate mechanisms of this neuroprotection, we evaluated changes in gene expression using microarrays comparing differences before and after MCAO, and differences between genotypes. Genes associated with inflammation and cell deaths were enriched after MCAO in both genotypes, and we identified several genes not previously shown to increase following ischaemia (Ccl9, Gem and Prg4). In addition to networks that were similar between genotypes, one network with a central core of GPCR (G-protein-coupled receptor) and including biological functions such as carbohydrate metabolism, small molecule biochemistry and inflammation was identified in FVB mice but not in $beta$2KO mice. Analysis of differences between genotypes revealed 11 genes differentially expressed by genotype both before and after ischaemia. We demonstrate greater Glo1 protein levels and lower Pmaip/Noxa mRNA levels in $beta$2KO mice in both sham and MCAO conditions. As both genes are implicated in NF-$kappa$B (nuclear factor $kappa$B) signalling, we measured p65 activity and TNF$alpha$ (tumour necrosis factor $alpha$) levels 24 h after MCAO. MCAO-induced p65 activation and post-ischaemic TNF$alpha$ production were both greater in FVB compared with $beta$2KO mice. These results suggest that loss of $beta$2AR signaling results in a neuroprotective phenotype in part due to decreased NF-$kappa$B signalling, decreased inflammation and decreased apoptotic signalling in the brain.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The role of the $beta$2AR ($beta$2 adrenergic receptor) after stroke is unclear as pharmacological manipulations of the $beta$2AR have produced contradictory results. We previously showed that mice deficient in the $beta$2AR ($beta$2KO) had smaller infarcts compared with WT (wild-type) mice (FVB) after MCAO (middle cerebral artery occlusion), a model of stroke. To elucidate mechanisms of this neuroprotection, we evaluated changes in gene expression using microarrays comparing differences before and after MCAO, and differences between genotypes. Genes associated with inflammation and cell deaths were enriched after MCAO in both genotypes, and we identified several genes not previously shown to increase following ischaemia (Ccl9, Gem and Prg4). In addition to networks that were similar between genotypes, one network with a central core of GPCR (G-protein-coupled receptor) and including biological functions such as carbohydrate metabolism, small molecule biochemistry and inflammation was identified in FVB mice but not in $beta$2KO mice. Analysis of differences between genotypes revealed 11 genes differentially expressed by genotype both before and after ischaemia. We demonstrate greater Glo1 protein levels and lower Pmaip/Noxa mRNA levels in $beta$2KO mice in both sham and MCAO conditions. As both genes are implicated in NF-$kappa$B (nuclear factor $kappa$B) signalling, we measured p65 activity and TNF$alpha$ (tumour necrosis factor $alpha$) levels 24 h after MCAO. MCAO-induced p65 activation and post-ischaemic TNF$alpha$ production were both greater in FVB compared with $beta$2KO mice. These results suggest that loss of $beta$2AR signaling results in a neuroprotective phenotype in part due to decreased NF-$kappa$B signalling, decreased inflammation and decreased apoptotic signalling in the brain. |
Ruoting Yang; Bernie J Daigle Jr.; Linda R Petzold; Francis J Doyle III Core Module Network Construction for Breast Cancer Metastasis Inproceedings 2012 10th World Congress on Intelligent Control and Automation (WCICA), pp. 5083–5089, 2012. @inproceedings{yang_core_2012, title = {Core Module Network Construction for Breast Cancer Metastasis}, author = {Ruoting Yang and Bernie J Daigle Jr. and Linda R Petzold and Francis J Doyle III}, doi = {10.1109/WCICA.2012.6359441}, year = {2012}, date = {2012-01-01}, booktitle = {2012 10th World Congress on Intelligent Control and Automation (WCICA)}, pages = {5083--5089}, abstract = {For prognostic and diagnostic purposes, it is crucial to be able to separate the group of ``driver'' genes and their first-degree neighbours, (i.e. ``core module'') from the general ``disease module''. To facilitate this task, we developed a novel computational framework COMBINER: COre Module Biomarker Identification with Network ExploRation. We applied COMBINER to three benchmark breast cancer datasets for identifying prognostic biomarkers. We generated a list of ``driver genes'' by finding the common core modules between two sets of COMBINER markers identified with different module inference protocols. Overlaying the markers on the map of ``the hallmarks of cancer'' and constructing a weighted regulatory network with sensitivity analysis, we validated 29 driver genes. Our results show the COMBINER framework to be a promising approach for identifying and characterizing core modules and driver genes of many complex diseases.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } For prognostic and diagnostic purposes, it is crucial to be able to separate the group of ``driver'' genes and their first-degree neighbours, (i.e. ``core module'') from the general ``disease module''. To facilitate this task, we developed a novel computational framework COMBINER: COre Module Biomarker Identification with Network ExploRation. We applied COMBINER to three benchmark breast cancer datasets for identifying prognostic biomarkers. We generated a list of ``driver genes'' by finding the common core modules between two sets of COMBINER markers identified with different module inference protocols. Overlaying the markers on the map of ``the hallmarks of cancer'' and constructing a weighted regulatory network with sensitivity analysis, we validated 29 driver genes. Our results show the COMBINER framework to be a promising approach for identifying and characterizing core modules and driver genes of many complex diseases. |
2011 |
Min K Roh; Bernie J Daigle Jr.; Dan T Gillespie; Linda R Petzold State-Dependent Doubly Weighted Stochastic Simulation Algorithm for Automatic Characterization of Stochastic Biochemical Rare Events Journal Article The Journal of Chemical Physics, 135 (23), pp. 234108, 2011, ISSN: 0021-9606, 1089-7690. @article{roh_state-dependent_2011, title = {State-Dependent Doubly Weighted Stochastic Simulation Algorithm for Automatic Characterization of Stochastic Biochemical Rare Events}, author = {Min K Roh and Bernie J Daigle Jr. and Dan T Gillespie and Linda R Petzold}, doi = {10.1063/1.3668100}, issn = {0021-9606, 1089-7690}, year = {2011}, date = {2011-12-01}, urldate = {2015-10-16}, journal = {The Journal of Chemical Physics}, volume = {135}, number = {23}, pages = {234108}, abstract = {In recent years there has been substantial growth in the development of algorithms for characterizing rare events in stochastic biochemical systems. Two such algorithms, the state-dependent weighted stochastic simulation algorithm (swSSA) and the doubly weighted SSA (dwSSA) are extensions of the weighted SSA (wSSA) by H. Kuwahara and I. Mura [J. Chem. Phys.129, 165101 (2008)]10.1063/1.2987701. The swSSA substantially reduces estimator variance by implementing system state-dependent importance sampling (IS) parameters, but lacks an automatic parameter identification strategy. In contrast, the dwSSA provides for the automatic determination of state-independent IS parameters, thus it is inefficient for systems whose states vary widely in time. We present a novel modification of the dwSSA—the state-dependent doubly weighted SSA (sdwSSA)—that combines the strengths of the swSSA and the dwSSA without inheriting their weaknesses. The sdwSSA automatically computes state-dependent IS parameters via the multilevel cross-entropy method. We apply the method to three examples: a reversible isomerization process, a yeast polarization model, and a lac operon model. Our results demonstrate that the sdwSSA offers substantial improvements over previous methods in terms of both accuracy and efficiency.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In recent years there has been substantial growth in the development of algorithms for characterizing rare events in stochastic biochemical systems. Two such algorithms, the state-dependent weighted stochastic simulation algorithm (swSSA) and the doubly weighted SSA (dwSSA) are extensions of the weighted SSA (wSSA) by H. Kuwahara and I. Mura [J. Chem. Phys.129, 165101 (2008)]10.1063/1.2987701. The swSSA substantially reduces estimator variance by implementing system state-dependent importance sampling (IS) parameters, but lacks an automatic parameter identification strategy. In contrast, the dwSSA provides for the automatic determination of state-independent IS parameters, thus it is inefficient for systems whose states vary widely in time. We present a novel modification of the dwSSA—the state-dependent doubly weighted SSA (sdwSSA)—that combines the strengths of the swSSA and the dwSSA without inheriting their weaknesses. The sdwSSA automatically computes state-dependent IS parameters via the multilevel cross-entropy method. We apply the method to three examples: a reversible isomerization process, a yeast polarization model, and a lac operon model. Our results demonstrate that the sdwSSA offers substantial improvements over previous methods in terms of both accuracy and efficiency. |
Bernie J Daigle Jr.; Min K Roh; Dan T Gillespie; Linda R Petzold Automated Estimation of Rare Event Probabilities in Biochemical Systems Journal Article The Journal of Chemical Physics, 134 (4), pp. 044110, 2011, ISSN: 0021-9606, 1089-7690. @article{daigle_jr._automated_2011, title = {Automated Estimation of Rare Event Probabilities in Biochemical Systems}, author = {Bernie J Daigle Jr. and Min K Roh and Dan T Gillespie and Linda R Petzold}, doi = {10.1063/1.3522769}, issn = {0021-9606, 1089-7690}, year = {2011}, date = {2011-01-01}, urldate = {2015-10-16}, journal = {The Journal of Chemical Physics}, volume = {134}, number = {4}, pages = {044110}, abstract = {In biochemical systems, the occurrence of a rare event can be accompanied by catastrophic consequences. Precise characterization of these events using Monte Carlo simulation methods is often intractable, as the number of realizations needed to witness even a single rare event can be very large. The weighted stochastic simulation algorithm (wSSA) [J. Chem. Phys.129, 165101 (2008)] and its subsequent extension [J. Chem. Phys.130, 174103 (2009)] alleviate this difficulty with importance sampling, which effectively biases the system toward the desired rare event. However, extensive computation coupled with substantial insight into a given system is required, as there is currently no automatic approach for choosing wSSA parameters. We present a novel modification of the wSSA—the doubly weighted SSA (dwSSA)—that makes possible a fully automated parameter selection method. Our approach uses the information-theoretic concept of cross entropy to identify parameter values yielding minimum variance rare event probability estimates. We apply the method to four examples: a pure birth process, a birth-death process, an enzymatic futile cycle, and a yeast polarization model. Our results demonstrate that the proposed method (1) enables probability estimation for a class of rare events that cannot be interrogated with the wSSA, and (2) for all examples tested, reduces the number of runs needed to achieve comparable accuracy by multiple orders of magnitude. For a particular rare event in the yeast polarization model, our method transforms a projected simulation time of 600 years to three hours. Furthermore, by incorporating information-theoretic principles, our approach provides a framework for the development of more sophisticated influencing schemes that should further improve estimation accuracy.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In biochemical systems, the occurrence of a rare event can be accompanied by catastrophic consequences. Precise characterization of these events using Monte Carlo simulation methods is often intractable, as the number of realizations needed to witness even a single rare event can be very large. The weighted stochastic simulation algorithm (wSSA) [J. Chem. Phys.129, 165101 (2008)] and its subsequent extension [J. Chem. Phys.130, 174103 (2009)] alleviate this difficulty with importance sampling, which effectively biases the system toward the desired rare event. However, extensive computation coupled with substantial insight into a given system is required, as there is currently no automatic approach for choosing wSSA parameters. We present a novel modification of the wSSA—the doubly weighted SSA (dwSSA)—that makes possible a fully automated parameter selection method. Our approach uses the information-theoretic concept of cross entropy to identify parameter values yielding minimum variance rare event probability estimates. We apply the method to four examples: a pure birth process, a birth-death process, an enzymatic futile cycle, and a yeast polarization model. Our results demonstrate that the proposed method (1) enables probability estimation for a class of rare events that cannot be interrogated with the wSSA, and (2) for all examples tested, reduces the number of runs needed to achieve comparable accuracy by multiple orders of magnitude. For a particular rare event in the yeast polarization model, our method transforms a projected simulation time of 600 years to three hours. Furthermore, by incorporating information-theoretic principles, our approach provides a framework for the development of more sophisticated influencing schemes that should further improve estimation accuracy. |
2010 |
Jesse M Engreitz; Bernie J Daigle Jr.; Jonathan J Marshall; Russ B Altman Independent Component Analysis: Mining Microarray Data for Fundamental Human Gene Expression Modules Journal Article Journal of Biomedical Informatics, 43 (6), pp. 932–944, 2010, ISSN: 1532-0464. @article{engreitz_independent_2010, title = {Independent Component Analysis: Mining Microarray Data for Fundamental Human Gene Expression Modules}, author = {Jesse M Engreitz and Bernie J Daigle Jr. and Jonathan J Marshall and Russ B Altman}, doi = {10.1016/j.jbi.2010.07.001}, issn = {1532-0464}, year = {2010}, date = {2010-01-01}, urldate = {2015-10-16}, journal = {Journal of Biomedical Informatics}, volume = {43}, number = {6}, pages = {932--944}, abstract = {As public microarray repositories rapidly accumulate gene expression data, these resources contain increasingly valuable information about cellular processes in human biology. This presents a unique opportunity for intelligent data mining methods to extract information about the transcriptional modules underlying these biological processes. Modeling cellular gene expression as a combination of functional modules, we use independent component analysis (ICA) to derive 423 fundamental components of human biology from a 9395-array compendium of heterogeneous expression data. Annotation using the Gene Ontology (GO) suggests that while some of these components represent known biological modules, others may describe biology not well characterized by existing manually-curated ontologies. In order to understand the biological functions represented by these modules, we investigate the mechanism of the preclinical anti-cancer drug parthenolide (PTL) by analyzing the differential expression of our fundamental components. Our method correctly identifies known pathways and predicts that N-glycan biosynthesis and T-cell receptor signaling may contribute to PTL response. The fundamental gene modules we describe have the potential to provide pathway-level insight into new gene expression datasets.}, keywords = {}, pubstate = {published}, tppubtype = {article} } As public microarray repositories rapidly accumulate gene expression data, these resources contain increasingly valuable information about cellular processes in human biology. This presents a unique opportunity for intelligent data mining methods to extract information about the transcriptional modules underlying these biological processes. Modeling cellular gene expression as a combination of functional modules, we use independent component analysis (ICA) to derive 423 fundamental components of human biology from a 9395-array compendium of heterogeneous expression data. Annotation using the Gene Ontology (GO) suggests that while some of these components represent known biological modules, others may describe biology not well characterized by existing manually-curated ontologies. In order to understand the biological functions represented by these modules, we investigate the mechanism of the preclinical anti-cancer drug parthenolide (PTL) by analyzing the differential expression of our fundamental components. Our method correctly identifies known pathways and predicts that N-glycan biosynthesis and T-cell receptor signaling may contribute to PTL response. The fundamental gene modules we describe have the potential to provide pathway-level insight into new gene expression datasets. |
Bernie J Daigle Jr.; Alicia Deng; Tracey McLaughlin; Samuel W Cushman; Margaret C Cam; Gerald Reaven; Philip S Tsao; Russ B Altman Using Pre-Existing Microarray Datasets to Increase Experimental Power: Application to Insulin Resistance Journal Article PLoS Comput Biol, 6 (3), pp. e1000718, 2010. @article{daiglejr._using_2010, title = {Using Pre-Existing Microarray Datasets to Increase Experimental Power: Application to Insulin Resistance}, author = {Bernie J Daigle Jr. and Alicia Deng and Tracey McLaughlin and Samuel W Cushman and Margaret C Cam and Gerald Reaven and Philip S Tsao and Russ B Altman}, doi = {10.1371/journal.pcbi.1000718}, year = {2010}, date = {2010-01-01}, urldate = {2015-10-16}, journal = {PLoS Comput Biol}, volume = {6}, number = {3}, pages = {e1000718}, abstract = {Author Summary Though the use of microarrays to identify differentially expressed (DE) genes has become commonplace, it is still not a trivial task. Microarray data are notorious for being noisy, and current DE gene methods do not fully utilize pre-existing biological knowledge to help control this noise. One such source of knowledge is the vast number of publicly available microarray datasets. To leverage this information, we have developed the SVD Augmented Gene expression Analysis Tool (SAGAT) for identifying DE genes. SAGAT extracts transcriptional modules from publicly available microarray data and integrates this information with a dataset of interest. We explore SAGAT's ability to improve DE gene identification on simulated data, and we validate the method on three highly replicated biological datasets. Finally, we demonstrate SAGAT's effectiveness on a novel human dataset investigating the transcriptional response to insulin resistance. Use of SAGAT leads to an increased number of insulin resistant candidate genes, and we validate a subset of these with qPCR. We provide SAGAT as an open source R package that is applicable to any human microarray study.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Author Summary Though the use of microarrays to identify differentially expressed (DE) genes has become commonplace, it is still not a trivial task. Microarray data are notorious for being noisy, and current DE gene methods do not fully utilize pre-existing biological knowledge to help control this noise. One such source of knowledge is the vast number of publicly available microarray datasets. To leverage this information, we have developed the SVD Augmented Gene expression Analysis Tool (SAGAT) for identifying DE genes. SAGAT extracts transcriptional modules from publicly available microarray data and integrates this information with a dataset of interest. We explore SAGAT's ability to improve DE gene identification on simulated data, and we validate the method on three highly replicated biological datasets. Finally, we demonstrate SAGAT's effectiveness on a novel human dataset investigating the transcriptional response to insulin resistance. Use of SAGAT leads to an increased number of insulin resistant candidate genes, and we validate a subset of these with qPCR. We provide SAGAT as an open source R package that is applicable to any human microarray study. |
Bernie J Daigle Jr.; Balaji S Srinivasan; Jason A Flannick; Antal F Novak; Serafim Batzoglou Current Progress in Static and Dynamic Modeling of Biological Networks Incollection Sangdun Choi (Ed.): Systems Biology for Signaling Networks, pp. 13–73, Springer New York, 2010, ISBN: 978-1-4419-5796-2 978-1-4419-5797-9. @incollection{daigle_jr._current_2010, title = {Current Progress in Static and Dynamic Modeling of Biological Networks}, author = {Bernie J Daigle Jr. and Balaji S Srinivasan and Jason A Flannick and Antal F Novak and Serafim Batzoglou}, editor = {Sangdun Choi}, doi = {10.1007/978-1-4419-5797-9_2}, isbn = {978-1-4419-5796-2 978-1-4419-5797-9}, year = {2010}, date = {2010-01-01}, urldate = {2015-10-16}, booktitle = {Systems Biology for Signaling Networks}, pages = {13--73}, publisher = {Springer New York}, series = {Systems Biology}, abstract = {The relentless advance of biochemistry has enabled us to take apart biological systems with ever more fine-grained and precise instruments. The fruits of this dissection are millions of measurements of base pairs and biochemical concentrations. Yet to make sense of these numbers, we need to reverse our dissection by putting the system back together on the computer. This first step in this process is reconstructing molecular anatomy through static modeling, the determination of which pieces (DNA, RNA, protein, and metabolite) is present, and how they are related (e.g., regulator, target, inhibitor, cofactor). Given this broad outline of component connectivity, we may then attempt to reconstruct molecular physiology via dynamic modeling, computer simulations that model when cellular events occur (ODE), where they occur (PDE), and how frequently they recur (SDE). In this review we discuss techniques for both of these modeling paradigms, illustrating each by reference to important recent papers.}, keywords = {}, pubstate = {published}, tppubtype = {incollection} } The relentless advance of biochemistry has enabled us to take apart biological systems with ever more fine-grained and precise instruments. The fruits of this dissection are millions of measurements of base pairs and biochemical concentrations. Yet to make sense of these numbers, we need to reverse our dissection by putting the system back together on the computer. This first step in this process is reconstructing molecular anatomy through static modeling, the determination of which pieces (DNA, RNA, protein, and metabolite) is present, and how they are related (e.g., regulator, target, inhibitor, cofactor). Given this broad outline of component connectivity, we may then attempt to reconstruct molecular physiology via dynamic modeling, computer simulations that model when cellular events occur (ODE), where they occur (PDE), and how frequently they recur (SDE). In this review we discuss techniques for both of these modeling paradigms, illustrating each by reference to important recent papers. |
2008 |
Bernie J Daigle Jr.; Russ B Altman M-BISON: Microarray-Based Integration of Data Sources Using Networks Journal Article BMC Bioinformatics, 9 (1), pp. 214, 2008, ISSN: 1471-2105. @article{daiglejr._mbison_2008, title = {M-BISON: Microarray-Based Integration of Data Sources Using Networks}, author = {Bernie J Daigle Jr. and Russ B Altman}, doi = {10.1186/1471-2105-9-214}, issn = {1471-2105}, year = {2008}, date = {2008-04-01}, urldate = {2015-10-16}, journal = {BMC Bioinformatics}, volume = {9}, number = {1}, pages = {214}, abstract = {The accurate detection of differentially expressed (DE) genes has become a central task in microarray analysis. Unfortunately, the noise level and experimental variability of microarrays can be limiting. While a number of existing methods partially overcome these limitations by incorporating biological knowledge in the form of gene groups, these methods sacrifice gene-level resolution. This loss of precision can be inappropriate, especially if the desired output is a ranked list of individual genes. To address this shortcoming, we developed M-BISON (Microarray-Based Integration of data SOurces using Networks), a formal probabilistic model that integrates background biological knowledge with microarray data to predict individual DE genes.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The accurate detection of differentially expressed (DE) genes has become a central task in microarray analysis. Unfortunately, the noise level and experimental variability of microarrays can be limiting. While a number of existing methods partially overcome these limitations by incorporating biological knowledge in the form of gene groups, these methods sacrifice gene-level resolution. This loss of precision can be inappropriate, especially if the desired output is a ranked list of individual genes. To address this shortcoming, we developed M-BISON (Microarray-Based Integration of data SOurces using Networks), a formal probabilistic model that integrates background biological knowledge with microarray data to predict individual DE genes. |
2004 |
Stephen R Yant; Xiaolin Wu; Yong Huang; Bernie J Daigle Jr.; Brian A Garrison; Shawn M Burgess; Mark A Kay Nonrandom Insertion Site Preferences for the SB Transposon in Vitro and in Vivo Journal Article Molecular Therapy, 9 , pp. S309–S310, 2004. @article{Yant:2004p89, title = {Nonrandom Insertion Site Preferences for the SB Transposon in Vitro and in Vivo}, author = {Stephen R Yant and Xiaolin Wu and Yong Huang and Bernie J Daigle Jr. and Brian A Garrison and Shawn M Burgess and Mark A Kay}, year = {2004}, date = {2004-01-01}, journal = {Molecular Therapy}, volume = {9}, pages = {S309--S310}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
2003 |
Stephen R Yant; Brian A Garrison; Bernie J Daigle Jr.; Mark A Kay Analysis of Target Site Selection for the Sleeping Beauty Transposon in Mouse Liver Journal Article Molecular Therapy, 7 (5), pp. S63–S63, 2003. @article{Yant:2003p96, title = {Analysis of Target Site Selection for the Sleeping Beauty Transposon in Mouse Liver}, author = {Stephen R Yant and Brian A Garrison and Bernie J Daigle Jr. and Mark A Kay}, year = {2003}, date = {2003-01-01}, journal = {Molecular Therapy}, volume = {7}, number = {5}, pages = {S63--S63}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
2002 |
Garth D Ehrlich; Richard Veeh; Xue Wang; William J Costerton; Jay D Hayes; Fen Z Hu; Bernie J Daigle Jr.; Miles D Ehrlich; Christopher J Post Mucosal Biofilm Formation on Middle-Ear Mucosa in the Chinchilla Model of Otitis Media Journal Article JAMA, 287 (13), pp. 1710–1715, 2002, ISSN: 0098-7484. @article{d._mucosal_2002, title = {Mucosal Biofilm Formation on Middle-Ear Mucosa in the Chinchilla Model of Otitis Media}, author = {Garth D Ehrlich and Richard Veeh and Xue Wang and William J Costerton and Jay D Hayes and Fen Z Hu and Bernie J Daigle Jr. and Miles D Ehrlich and Christopher J Post}, doi = {10.1001/jama.287.13.1710}, issn = {0098-7484}, year = {2002}, date = {2002-01-01}, urldate = {2015-10-16}, journal = {JAMA}, volume = {287}, number = {13}, pages = {1710--1715}, abstract = {Context~Chronic otitis media with effusion (OME) has long been considered to be a sterile inflammatory process. The previous application of molecular diagnostic technologies to OME suggests that viable bacteria are present in complex communities known as mucosal biofilms; however, direct imaging evidence of mucosal biofilms associated with OM is lacking.Objective~To determine whether biofilm formation occurs in middle-ear mucosa in an experimental model of otitis media.Design and Materials~A total of 48 research-grade, young adult chinchillas weighing 500 g were used for 2 series of animal experiments: one to obtain specimens for scanning electron microscopy and the other to obtain specimens for confocal laser scanning microscopy using vital dyes. In each series, 21 animals were bilaterally injected with viable Haemophilus influenzae bacteria and 1 was inoculated to account for expected mortality. Three served as negative controls. Effusions and mucosal specimens were collected from 2 infected animals that were euthanized at 3, 6, 12, and 24 hours and at days 2, 4, 5, 10, 16, and 22 after inoculation.Main Outcome Measures~Images were analyzed for biofilm morphology, including presence of microcolony formation and for presence of bacteria on tissue surfaces.Results~Scanning electron microscopy demonstrated that biofilm formation was evident in all specimens from animals beginning 1 day after infection and was present through 21 days. Confocal laser scanning microscopy indicated that bacteria within the biofilms are viable.Conclusion~These preliminary findings provide evidence that mucosal biofilms form in an experimental model of otitis media and suggest that biofilm formation may be an important factor in the pathogenesis of chronic otitis media with effusion.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Context~Chronic otitis media with effusion (OME) has long been considered to be a sterile inflammatory process. The previous application of molecular diagnostic technologies to OME suggests that viable bacteria are present in complex communities known as mucosal biofilms; however, direct imaging evidence of mucosal biofilms associated with OM is lacking.Objective~To determine whether biofilm formation occurs in middle-ear mucosa in an experimental model of otitis media.Design and Materials~A total of 48 research-grade, young adult chinchillas weighing 500 g were used for 2 series of animal experiments: one to obtain specimens for scanning electron microscopy and the other to obtain specimens for confocal laser scanning microscopy using vital dyes. In each series, 21 animals were bilaterally injected with viable Haemophilus influenzae bacteria and 1 was inoculated to account for expected mortality. Three served as negative controls. Effusions and mucosal specimens were collected from 2 infected animals that were euthanized at 3, 6, 12, and 24 hours and at days 2, 4, 5, 10, 16, and 22 after inoculation.Main Outcome Measures~Images were analyzed for biofilm morphology, including presence of microcolony formation and for presence of bacteria on tissue surfaces.Results~Scanning electron microscopy demonstrated that biofilm formation was evident in all specimens from animals beginning 1 day after infection and was present through 21 days. Confocal laser scanning microscopy indicated that bacteria within the biofilms are viable.Conclusion~These preliminary findings provide evidence that mucosal biofilms form in an experimental model of otitis media and suggest that biofilm formation may be an important factor in the pathogenesis of chronic otitis media with effusion. |