Apr 10
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Veera Baladandayuthapani

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Dr. Veera Baladandayuthapani is currently a Professor in the Department of Biostatistics at University of Michigan (UM), where he is also the Associate Director of the Center for Cancer Biostatistics. He joined UM in Fall 2018 after spending 13 years in the Department of Biostatistics at University of Texas MD Anderson Cancer Center, Houston, Texas, where was a Professor and Institute Faculty Scholar and held adjunct appointments at Rice University, Texas A&M University and UT School of Public Health. His research interests are mainly in high-dimensional data modeling and Bayesian inference. This includes functional data analyses, Bayesian graphical models, Bayesian semi-/non-parametric models and Bayesian machine learning. These methods are motivated by large and complex datasets (a.k.a. Big Data) such as high-throughput genomics, epigenomics, transcriptomics and proteomics as well as high-resolution neuro- and cancer- imaging. His work has been published in top statistical/biostatistical/bioinformatics and biomedical/oncology journals. He has also co-authored a book on Bayesian analysis of gene expression data. He currently holds multiple PI-level grants from NIH and NSF to develop innovative and advanced biostatistical and bioinformatics methods for big datasets in oncology. He has also served as the Director of the Biostatistics and Bioinformatics Cores for the Specialized Programs of Research Excellence (SPOREs) in Multiple Myeloma and Lung Cancer and Biostatistics&Bioinformatics platform leader for the Myeloma and Melanoma Moonshot Programs at MD Anderson. He is a fellow of the American Statistical Association and an elected member of the International Statistical Institute. He currently serves as an Associate Editor for Journal of American Statistical Association, Biometrics and Sankhya.

 

An example of horizontal (across cancers) and vertical (across multiple molecular platforms) data integration. Image from Ha et al (Nature Scientific Reports, 2018; https://www.nature.com/articles/s41598-018-32682-x)

Jan 04
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Jun Li

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Jun Li, PhD, is Professor and Chair for Research in the department of Computational Medicine and Bioinformatics and Professor of Human Genetics in the Medical School at the University of Michigan, Ann Arbor.

 Prof. Li’s areas of interest include genetic and genomic analyses of complex phenotypes, including bipolar disorder, cancer, blood clotting disease, and traits involving animal models and human microbiomes. Our approach emphasizes statistical analysis of genome-scale datasets (e.g, gene expression and genotyping data, results from next-generation sequencing), evolutionary history, bioinformatics, and pattern recognition.
Dec 21
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Danny Forger

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Daniel Forger is a Professor in the Department of Mathematics. He is devoted to understanding biological clocks. He uses techniques from many fields, including computer simulation, detailed mathematical modeling and mathematical analysis, to understand biological timekeeping. His research aims to generate predictions that can be experimentally verified.

Mar 08
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Gilbert S. Omenn

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Gilbert Omenn, MD, PhD, is Professor of Computational Medicine & Bioinformatics with appointments in Human Genetics, Molecular Medicine & Genetics in the Medical School and Professor of Public Health in the School of Public Health and the Harold T. Shapiro Distinguished University Professor at the University of Michigan, Ann Arbor.

Doctor Omenn’s current research interests are focused on cancer proteomics, splice isoforms as potential biomarkers and therapeutic tar- gets, and isoform-level and single-cell functional networks of transcripts and proteins. He chairs the global Human Proteome Project of the Human Proteome Organization.

Mar 08
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Jeremy M G Taylor

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Jeremy Taylor, PhD, is the Pharmacia Research Professor of Biostatistics in the School of Public Health and Professor in the Department of Radiation Oncology in the School of Medicine at the University of Michigan, Ann Arbor. He is the director of the University of Michigan Cancer Center Biostatistics Unit and director of the Cancer/Biostatistics training program. He received his B.A. in Mathematics from Cambridge University and his Ph.D. in Statistics from UC Berkeley. He was on the faculty at UCLA from 1983 to 1998, when he moved to the University of Michigan. He has had visiting positions at the Medical Research Council, Cambridge, England; the University of Adelaide; INSERM, Bordeaux and CSIRO, Sydney, Australia. He is a previously winner of the Mortimer Spiegelman Award from the American Public Health Association and the Michael Fry Award from the Radiation Research Society. He has worked in various areas of Statistics and Biostatistics, including Box-Cox transformations, longitudinal and survival analysis, cure models, missing data, smoothing methods, clinical trial design, surrogate and auxiliary variables. He has been heavily involved in collaborations in the areas of radiation oncology, cancer research and bioinformatics.

I have broad interests and expertise in developing statistical methodology and applying it in biomedical research, particularly in cancer research. I have undertaken research  in power transformations, longitudinal modeling, survival analysis particularly cure models, missing data methods, causal inference and in modeling radiation oncology related data.  Recent interests, specifically related to cancer, are in statistical methods for genomic data, statistical methods for evaluating cancer biomarkers, surrogate endpoints, phase I trial design, statistical methods for personalized medicine and prognostic and predictive model validation.  I strive to develop principled methods that will lead to valid interpretations of the complex data that is collected in biomedical research.

Mar 08
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Jieping Ye

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Jieping Ye, PhD, is Associate Professor of Computational Medicine and Bioinformatics in the Medical School at the University of Michigan, Ann Arbor.

The Ye Lab has been conducting fundamental research in machine learning and data mining, developing computational methods for biomedical data analysis, and building informatics software. We have developed novel machine learning algorithms for feature extraction from high-dimensional data, sparse learning, multi-task learning, transfer learning, active learning, multi-label classification, and matrix completion. We have developed the SLEP (Sparse Learning with Efficient Projections) package, which includes implementations of large-scale sparse learning models, and the MALSAR (Multi-tAsk Learning via StructurAl Regularization) package, which includes implementations of state-of-the-art multi-task learning models. SLEP achieves state-of-the-art performance for many sparse learning models, and it has become one of the most popular sparse learning software packages. With close collaboration with researchers at the biomedical field, we have successfully applied these methods for analyzing biomedical data, including clinical image data and genotype data.

Mar 08
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Indika Rajapakse

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Indika Rajapakse, PhD, is Associate Professor of Computational Medicine & Bioinformatics, Medical School, with a secondary appointment in Mathematics, College of Literature, Science, and the Arts at the University of Michigan, Ann Arbor.

Prof. Rajapakse’s research is focused on the dynamics of genome organization in human cells, with emphasis on gaining a deeper understanding of how the cell cycle guides cell fate determination. Prof. Rajapakse and his team are developing genomic and imaging technologies for determining the natural dynamics of the cell cycle and building a data guided mathematical foundation. Their long term goal is to develop strategies for direct reprogramming of normal and abnormal cells

Mar 08
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Brian D. Athey

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The Athey Lab in the Department of Computational Medicine and Bioinformatics (DCM&B) University of Michigan Medical School, is led by Dr. Brian Athey (see Atheylab.ccmb.med.umich.edu).

The lab is working on two complementary domains of research and development.

1. The Athey Lab’s recent research interests are in the creation and use of bioinformatics pipelines and machine learning methods to radically improve the efficacy of psychiatric pharmacogenomics—allowing patients to take the most effective drug for their illness and suffer the fewest side effects. This area of research centers on the exploration of the ‘pharmacoepigenome’ in psychiatry, neurology, anesthesia and addiction medicine. This research employs high-throughput 4D microscopic imaging of enhancers, promoters and chromatin features, using fluorescence in situ hybridization (FISH). These methods are coupled with Hi-C chromatin conformation capture, chromatin state annotation, localization in postmortem human brain tissue and induced neuronal pluripotent stem cells, and machine learning for identification of regulatory variants, to provide insight into the genetic and epigenetic mechanisms of inter-individual and inter-cohort differences in psychotropic drug response

2. The Athey Lab is also developing new high-throughput methods to analyze images of genes in the context of the cellular nucleus to better understand the machinery of bioinformatics in context. One main area of research is the application of high resolution fluorescence optical microscopy coupled with high-throughput analysis, 3D imaging and machine learning to explore the chromatin structure and nuclear architecture of cells. This research emphasizes the convergence between 3D structural predictions and 3D structural measurements with microscopy, to provide insight into the transcriptional architecture of the interphase nucleus.

This area of research centers on the exploration of the ‘pharmacoepigenome’ in psychiatry, neurology, anesthesia and addiction medicine. This research employs high-throughput 4D microscopic imaging of enhancers, promoters and chromatin features, using fluorescence in situ hybridization (FISH). These methods are coupled with Hi-C chromatin conformation capture, chromatin state annotation, localization in postmortem human brain tissue and induced neuronal pluripotent stem cells, and machine learning for identification of regulatory variants, to provide insight into the genetic and epigenetic mechanisms of inter-individual and inter-cohort differences in psychotropic drug response.

Collaborations: The lab works very closely with Assurex Health, Inc. (Mason, Ohio) on project 1. This work is governed by a Regents-approved Master Agreement between U-M and Assurex Health, Inc. Similarly, the lab collaborates closely with the tranSMART Foundation (tF), and this is also governed by a Master Agreement between U-M and tF.

The lab collaborates with the Brady Urological Institute at Johns Hopkins Medical School, lead by Dr. Ken Pienta, to build on their extensive 2D characterization of prostate tumors, by the introduction of simple chromatin dyes, advanced biomarkers, and 3D imaging systems.

The lab works closely with Dr. John Wiley of University of Michigan Health System, studying the effect of glucocorticoids on the neuroblastoma based cell line Sy5y before and after treatment with retinoic acid and BDNF, particularly in their terminally differentiated condition.

The lab also collaborates with Dr. Christoph Cremer from the Institute of Molecular Biology in Mainz, Germany, investigating super-resolution microscopy techniques.

Lithium response network in human brain in bipolar disorder: A regulatory network in human brain mediating lithium response in bipolar patients was revealed by analysis of functional single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS) and published gene association studies, followed by epigenome mapping. Noncoding SNPs characterized as altering enhancer and promoter function were imputed using fine epigenetic mapping, followed by bioinformatics analysis. Following gene set enrichment and pathway analysis, these genes were found to be significantly associated (p < 10-27; Fisher’s exact test) with this ionotropic AMPA2 glutamate receptor network in human brain67. Higgins GA, Allyn-Feuer A,Barbour E and Athey BD. “A glutamatergic network mediates lithium response in bipolar disorder as defined by epigenome pathway analysis.” Accepted, Journal of Pharmacogenomics. August 2015.

Lithium response network in human brain in bipolar disorder: A regulatory network in human brain mediating lithium response in bipolar patients was revealed by analysis of functional single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS) and published gene association studies, followed by epigenome mapping. Noncoding SNPs characterized as altering enhancer and promoter function were imputed using fine epigenetic mapping, followed by bioinformatics analysis. Following gene set enrichment and pathway analysis, these genes were found to be significantly associated (p < 10-27; Fisher’s exact test) with this ionotropic AMPA2 glutamate receptor network in human brain67. Higgins GA, Allyn-Feuer A,Barbour E and Athey BD. “A glutamatergic network mediates lithium response in bipolar disorder as defined by epigenome pathway analysis.” Accepted, Journal of Pharmacogenomics. August 2015.

Mar 08
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Ivo D. Dinov

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Dr. Ivo Dinov directs the Statistics Online Computational Resource (SOCR), co-directs the multi-institutional Probability Distributome Project, and is an associate director for education of the Michigan Institute for Data Science (MIDAS).

Dr. Dinov is an expert in mathematical modeling, statistical analysis, computational processing and visualization of Big Data. He is involved in longitudinal morphometric studies of human development (e.g., Autism, Schizophrenia), maturation (e.g., depression, pain) and aging (e.g., Alzheimer’s and Parkinson’s diseases). Dr. Dinov is developing, validating and disseminating novel technology-enhanced pedagogical approaches for scientific education and active learning.

9.9.2020 MIDAS Faculty Research Pitch Video.

Analyzing Big observational data including thousands of Parkinson's disease patients based on tens-of-thousands signature biomarkers derived from multi-source imaging, genetics, clinical, physiologic, phenomics and demographic data elements is challenging. We are developing Big Data representation strategies, implementing efficient algorithms and introducing software tools for managing, analyzing, modeling and visualizing large, complex, incongruent and heterogeneous data. Such service-oriented platforms and methodological advances enable Big Data Discovery Science and present existing opportunities for learners, educators, researchers, practitioners and policy makers.

Analyzing Big observational data including thousands of Parkinson’s disease patients based on tens-of-thousands signature biomarkers derived from multi-source imaging, genetics, clinical, physiologic, phenomics and demographic data elements is challenging. We are developing Big Data representation strategies, implementing efficient algorithms and introducing software tools for managing, analyzing, modeling and visualizing large, complex, incongruent and heterogeneous data. Such service-oriented platforms and methodological advances enable Big Data Discovery Science and present existing opportunities for learners, educators, researchers, practitioners and policy makers.