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Mousumi Banerjee

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My research is primarily focused around 1) machine learning methods for understanding healthcare delivery and outcomes in the population, 2) analyses of correlated data (e.g. longitudinal and clustered data), and 3) survival analysis and competing risks analyses. We have developed tree-based and ensemble regression methods for censored and multilevel data, combination classifiers using different types of learning methods, and methodology to identify representative trees from an ensemble. These methods have been applied to important areas of biomedicine, specifically in patient prognostication, in developing clinical decision-making tools, and in identifying complex interactions between patient, provider, and health systems for understanding variations in healthcare utilization and delivery. My substantive areas of research are cancer and pediatric cardiovascular disease.

Victoria Morckel

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Dr. Morckel uses spatial and statistical methods to examine ways to improve quality of life for people living in shrinking, deindustrialized cities in the Midwestern United States. She is especially interested in the causes and consequences of population loss, including issues of vacancy, blight, and neighborhood change.

Suitability Analysis Results: Map of Potential Properties to Naturalize in the City of Flint, Michigan.

Raed Al Kontar

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My research broadly focuses on developing data analytics and decision-making methodologies specifically tailored for Internet of Things (IoT) enabled smart and connected products/systems. I envision that most (if not all) engineering systems will eventually become connected systems in the future. Therefore, my key focus is on developing next-generation data analytics, machine learning, individualized informatics and graphical and network modeling tools to truly realize the competitive advantages that are promised by smart and connected products/systems.

 

Ho-Joon Lee

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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.

Figure 1. Proteomics and metabolomics analysis of IL-3 activation and cell cycle (Lee et al., Cell Reports 2017). (A) Multi-omics abundance profiles of proteins, modules/complexes, intracellular metabolites, and extracellular metabolites over one cell cycle (from left to right columns) in response to IL-3 activation. Red for proteins/modules/intracellular metabolites up-regulation or extracellular metabolites release; Green for proteins/modules/intracellular metabolites down-regulation or extracellular metabolites uptake. (B) Functional module network identified from integrative analysis. Red nodes are proteins and white nodes are functional modules. Expression profile plots are shown for literature-validated functional modules. (C) Overall pathway map of IL-3 activation and cell cycle phenotypes. (D) IL-3 activation and cell cycle as a cancer model along with candidate protein and metabolite biomarkers. (E) Protein co-expression scale-free network. (F) Power-low degree distribution of the network E. (G) Protein entropy distribution by topological Kolmogorov-Sinai entropy calculated for the network E.

 

S. Sriram

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S. Sriram, PhD, is Associate Professor of Marketing in the University of Michigan Ross School of Business, Ann Arbor.

Prof. Sriram’s research interests are in the areas of brand and product portfolio management, multi-sided platforms, healthcare policy, and online education. His research uses state of the art econometric methods to answer important managerial and policy-relevant questions. He has studied topics such as measuring and tracking brand equity and optimal allocation of resources to maintain long-term brand profitability, cannibalization, consumer adoption of technology products, and strategies for multi-sided platforms. Substantively, his research has spanned several industries including consumer packaged goods, technology products and services, retailing, news media, the interface of healthcare and marketing, and MOOCs.

Samuel K Handelman

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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

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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.

A visualization of data from the Michigan Genomics Initiative

“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.

Recent publications

From experts.umich.edu.

    Brenda Gillespie

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    Brenda Gillespie, PhD, is Associate Director in Consulting for Statistics, Computing and Analytics Research (CSCAR) with a secondary appointment as Associate Research Professor in the department of Biostatistics in the School of Public Health at the University of Michigan, Ann Arbor. She provides statistical collaboration and support for numerous research projects at the University of Michigan. She teaches Biostatistics courses as well as CSCAR short courses in survival analysis, regression analysis, sample size calculation, generalized linear models, meta-analysis, and statistical ethics. Her major areas of expertise are clinical trials and survival analysis.

    Prof. Gillespie’s research interests are in the area of censored data and clinical trials. One research interest concerns the application of categorical regression models to the case of censored survival data. This technique is useful in modeling the hazard function (instead of treating it as a nuisance parameter, as in Cox proportional hazards regression), or in the situation where time-related interactions (i.e., non-proportional hazards) are present. An investigation comparing various categorical modeling strategies is currently in progress.

    Another area of interest is the analysis of cross-over trials with censored data. Brenda has developed (with M. Feingold) a set of nonparametric methods for testing and estimation in this setting. Our methods out-perform previous methods in most cases.

    Adriene Beltz

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    The goal of my research is to leverage network analysis techniques to uncover how the brain mediates sex hormone influences on gendered behavior across the lifespan. Specifically, my data science research concerns the creation and application of person-specific connectivity analyses, such as unified structural equation models, to time series data; these are intensive longitudinal data, including functional neuroimages, daily diaries, and observations. I then use these data science methods to investigate the links between androgens (e.g., testosterone) and estradiol at key developmental periods, such as puberty, and behaviors that typically show sex differences, including aspects of cognition and psychopathology.

    A network map showing the directed connections among 25 brain regions of interest in the resting state frontoparietal network for an individual; data were acquired via functional magnetic resonance imaging. Black lines depict connections common across individuals in the sample, gray lines depict connections specific to this individual, solid lines depict contemporaneous connections (occurring in the same volume), and dashed lines depict lagged connections (occurring between volumes).

    A network map showing the directed connections among 25 brain regions of interest in the resting state frontoparietal network for an individual; data were acquired via functional magnetic resonance imaging. Black lines depict connections common across individuals in the sample, gray lines depict connections specific to this individual, solid lines depict contemporaneous connections (occurring in the same volume), and dashed lines depict lagged connections (occurring between volumes).