Dr. Bai’s research interests lie in development and refinement of bioinformatics algorithms/software and databases on next-generation sequencing (NGS data), development of statistical model for solving biological problems, bioinformatics analysis of clinical data, as well as other topics including, but not limited to, uncovering disease genes and variants using informatics approaches, computational analysis of cis-regulation and comparative motif finding, large-scale genome annotation, comparative “omics”, and evolutionary genomics.
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.
I study how law shapes innovation in the life sciences, with a substantial focus on big data and artificial intelligence in medicine. I write about the intellectual property incentives and protections for data and AI algorithms, the privacy issues with wide-scale health- and health-related data collection, the medical malpractice implications of AI in medicine, and how FDA should regulate the use of medical AI.
The focus of Dr. Najarian’s research is on the design of signal/image processing and machine learning methods to create computer-assisted clinical decision support systems that improve patient care and reduce the costs of healthcare. Dr. Najarian’s lab also designs sensors to collect and analyze physiological signals and images. In particular, Dr. Najarian’s research focuses on creating decision support systems to manage traumatic brain injuries, traumatic pelvic/abdominal injuries and hypovolemia. Dr. Najarian’s research has been funded by agencies such as National Science Foundation and Department of Defense. He serves as the Editor-in-Chief of Biomedical Engineering and Computational Biology and the Associate Editor of two other journals in the field of biomedical informatics. He is also a member of the editorial board of many other journals and serves as the guest editor of special issues for several journals.
Dr. Lee’s research in data science concerns biological questions in systems biology and network medicine by developing algorithms and models through a combination of statistical/machine learning, information theory, and network theory applied to multi-dimensional large-scale data. His projects have covered genomics, transcriptomics, proteomics, and metabolomics from yeast to mouse to human for integrative analysis of regulatory networks on multiple molecular levels, which also incorporates large-scale public databases such as GO for functional annotation, PDB for molecular structures, and PubChem and LINCS for drugs or small compounds. He previously carried out proteomics and metabolomics along with a computational derivation of dynamic protein complexes for IL-3 activation and cell cycle in murine pro-B cells (Lee et al., Cell Reports 2017), for which he developed integrative analytical tools using diverse approaches from machine learning and network theory. His ongoing interests in methodology include machine/deep learning and topological Kolmogorov-Sinai entropy-based network theory, which are applied to (1) multi-level dynamic regulatory networks in immune response, cell cycle, and cancer metabolism and (2) mass spectrometry-based omics data analysis.
Tim Cernak, PhD, is Assistant Professor of Medicinal Chemistry with secondary appointments in Chemistry and the Chemical Biology Program at the University of Michigan, Ann Arbor.
The functional and biological properties of a small molecule are encoded within its structure so synthetic strategies that access diverse structures are paramount to the invention of novel functional molecules such as biological probes, materials or pharmaceuticals. The Cernak Lab studies the interface of chemical synthesis and computer science to understand the relationship of structure, properties and reactions. We aim to use algorithms, robotics and big data to invent new chemical reactions, synthetic routes to natural products, and small molecule probes to answer questions in basic biology. Researchers in the group learn high-throughput chemical and biochemical experimentation, basic coding, and modern synthetic techniques. By studying the relationship of chemical synthesis to functional properties, we pursue the opportunity to positively impact human health.
Samuel K Handelman, Ph.D., is Research Assistant Professor in the department of Internal Medicine, Gastroenterology, of Michigan Medicine at the University of Michigan, Ann Arbor. Prof. Handelman is focused on multi-omics approaches to drive precision/personalized-therapy and to predict population-level differences in the effectiveness of interventions. He tends to favor regression-style and hierarchical-clustering approaches, partially because he has a background in both statistics and in cladistics. His scientific monomania is for compensatory mechanisms and trade-offs in evolution, but he has a principled reason to focus on translational medicine: real understanding of these mechanisms goes all the way into the clinic. Anything less that clinical translation indicates that we don’t understand what drove the genetics of human populations.
Zhenke Wu is an Assistant Professor of Biostatistics, and a core faculty member in the Michigan Institute of Data Science (MIDAS). He received his Ph.D. in Biostatistics from the Johns Hopkins University in 2014 and then stayed at Hopkins for his postdoctoral training before joining the University of Michigan. Dr. Wu’s research focuses on the design and application of statistical methods that inform health decisions made by individuals, or precision medicine. The original methods and software developed by Dr. Wu are now used by investigators from research institutes such as CDC and Johns Hopkins, as well as site investigators from developing countries, e.g., Kenya, South Africa, Gambia, Mali, Zambia, Thailand and Bangladesh.
Profile: At a “sweet spot” of data science
By Dan Meisler
Communications Manager, ARC
If you had to name two of the more exciting, emerging fields of data science, electronic health records (EHR) and mobile health might be near the top of the list.
Zhenke Wu, one of the newest MIDAS core faculty members, has one foot firmly in each field.
“These two fields share the common goal of learning from the experience of the population in the past to advance health and clinical decisions for those to follow. I am looking forward to more work that will bring the two fields closer to continuously generate insights about human health.” Wu said. “I’m in a sweet spot.”
Wu joined U-M in Fall 2016, after earning a PhD in Biostatistics from Johns Hopkins University, and a bachelor’s in Mathematics from Fudan University. He said the multitude of large-scale studies going on at U-M and access to EHR databases were factors in his coming to Michigan.
“The University of Michigan is an exciting place that has a diversity of large-scale databases and supportive research groups in the fields I’m interested in,” he said.
Wu is collaborating with the Michigan Genomics Initiative, which is a biorepository effort at Michigan Medicine to integrate genome-wide information with EHR from approximately 40,000 patients undergoing anesthesia prior to surgery or diagnostic procedures. He’s also collaborating with Dr. Srijan Sen, Associate Professor, Department of Psychiatry and Molecular and Behavioral Neuroscience Institute, on the MIDAS-supported project “Identifying Real-Time Data Predictors of Stress and Depression Using Mobile Technology,” the preliminary results of which recently matured into an NIH-funded R01 project “Mobile Technology to Identify Mechanisms Linking Genetic Variation and Depression” that will draw broad expertise from a multi-disciplinary team of medical and data science researchers.
“One of my goals is to use an integrated and rigorous approach to predict how a person’s health status will be in the near future,” Wu said.
Wu applies hierarchical Bayesian models to these problems, which he hopes will shed light on phenomena he describes as latent constructs that are “well-known, but less quantitatively understood, e.g., intelligence quotient (IQ) in psychology.”
As another example, he cites the current challenge in active surveillance of prostate cancer patients for aggressive tumors requiring removal and/or radiation, or indolent tumors permitting continued surveillance.
“The underlying status of aggressive versus indolent cancer is not observed, which needs to be learned from the results of biopsy and other clinical measurements,” he said. “The decisions and experience of urologists and their patients will greatly benefit from more accurate understanding of the tumor status… There are lots of scientific problems in clinical, biomedical, behavioral and social sciences where you have well-known but less quantitatively understood latent constructs. These are problems that Bayesian latent variable methods can formulate and address.”
Just as Wu has a hand in two hot-button big data areas, he also sees himself as straddling the line between application and methodology.
He says the large number of data sources — sensors, mobile apps, test results, and questionnaires, to name just a few — results in richness as well as some “messiness” that needs new methodologies to adjust, integrate and translate to new scientific insights. At the same time, a valid new methodology for dealing with, for example, electronic health data, will likely find numerous different applications.
Wu says his approach was heavily influenced by his work in the Pneumonia Etiology Research for Child Health (PERCH) funded by the Gates Foundation while he was at Johns Hopkins. Pneumonia is a clinical syndrome due to lung infection that can be caused by more than 30 different species of pathogens, including bacteria, viruses and fungi. The goal of the seven-country study that enrolled more than 5,000 cases and 5,000 controls from Africa and Southeast Asia is to estimate the frequency with which each pathogen caused pneumonia in the population and the probability of each individual being infected by the list of pathogens in the lung.
“In most settings, it is extremely difficult to identify the pathogen by directly sampling from the site of infection – the child’s lung. PERCH therefore looked for other sources of evidence by standardizing and comprehensively testing biofluids collected from sites peripheral to the lung. Using hierarchical Bayesian models to infer disease etiology by integrating such a large trove of data was extremely fun and exciting”, he said.
Wu’s initial interest in math, leading to biostatistics and now data science, stems from what he called a “greedy” desire to learn the guiding principles of how the world works by rigorous data science.
“If you have new problems, you can wait for other people to ask a clean math question, or you can go work with these messy problems and figure out interesting questions and their answers,” he said.
For more on Dr. Wu, see his profile on Michigan Experts.
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V.G.Vinod Vydiswaran, PhD, is Assistant Professor in the Department of Learning Health Sciences with a secondary appointment in the School of Information at the University of Michigan, Ann Arbor.
Dr. Vydiswaran’s research focuses on developing and applying text mining, natural language processing, and machine learning methodologies for extracting relevant information from health-related text corpora. This includes medically relevant information from clinical notes and biomedical literature, and studying the information quality and credibility of online health communication (via health forums and tweets). His previous work includes developing novel information retrieval models to assist clinical decision making, modeling information trustworthiness, and addressing the vocabulary gap between health professionals and laypersons.
Andrzej Galecki, MD, PhD, is Research Professor in the department of Biostatistics, School of Public Health, and Research Professor in the Institute of Gerontology at the University of Michigan, Ann Arbor.