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Patrick Schloss

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The Schloss lab is broadly interested in beneficial and pathogenic host-microbiome interactions with the goal of improving our understanding of how the microbiome can be used to reach translational outcomes in the prevention, detection, and treatment of colorectal cancer, Crohn’s disease, and Clostridium difficile infection. To address these questions, we test traditional ecological theory in the microbial context using a systems biology approach. Specifically, the laboratory specializes in using studies involving human subjects and animal models to understand how biological diversity affects community function using a variety of culture-independent genomics techniques including sequencing 16S rRNA gene fragments, metagenomics, and metatranscriptomics. In addition, they use metabolomics to understand the functional role of the gut microbiota in states of health and disease. To support these efforts, they develop and apply bioinformatic tools to facilitate their analysis. Most notable is the development of the mothur software package (https://www.mothur.org), which is one of the most widely used tools for analyzing microbiome data and has been cited more than 7,300 times since it was initially published in 2009. The Schloss lab deftly merges the ability to collect data to answer important biological questions using cutting edge wet-lab techniques and computational tools to synthesize these data to answer their biological questions.

Given the explosion in microbiome research over the past 15 years, the Schloss lab has also stood at the center of a major effort to train interdisciplinary scientists in applying computational tools to study complex biological systems. These efforts have centered around developing reproducible research skills and applying modern data visualization techniques. An outgrowth of these efforts at the University of Michigan has been the institutionalization of The Carpentries organization on campus (https://carpentries.org), which specializes in peer-to-peer instruction of programming tools and techniques to foster better reproducibility and build a community of practitioners.

The Schloss lab uses computational tools to integrate multi-omics tools in a culture-independent approach to understand how bacteria interact with each other and their host to drive processes such as colorectal cancer and susceptibility to Clostridium difficile infections.

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.

 

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.

Amal Alhosban

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Amal Alhosban, is an Assistant Professor of Computer Science at the University of Michigan Flint campus. She received her Ph.D. in Computer Science at Wayne State University in 2013. Her research focuses on Semantic Web and Fault Management and Wireless Network.

Bryan R. Goldsmith

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Bryan R. Goldsmith, PhD, is Assistant Professor in the department of Chemical Engineering within the College of Engineering at the University of Michigan, Ann Arbor.

Prof. Goldsmith’s research group utilizes first-principles modeling (e.g., density-functional theory and wave function based methods), molecular simulation, and data analytics tools (e.g., compressed sensing, kernel ridge regression, and subgroup discovery) to extract insights of catalysts and materials for sustainable chemical and energy production and to help create a platform for their design. For example, the group has exploited subgroup discovery as a data-mining approach to help find interpretable local patterns, correlations, and descriptors of a target property in materials-science data.  They also have been using compressed sensing techniques to find physically meaningful models that predict the properties of perovskite (ABX3) compounds.

Prof. Goldsmith’s areas of research encompass energy research, materials science, nanotechnology, physics, and catalysis.

A computational prediction for a group of gold nanoclusters (global model) could miss patterns unique to nonplaner clusters (subgroup 1) or planar clusters (subgroup 2).

A computational prediction for a group of gold nanoclusters (global model) could miss patterns unique to nonplaner clusters (subgroup 1) or planar clusters (subgroup 2).

 

V. G. Vinod Vydiswaran

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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.

Kevin Dombkowski

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Kevin J. Dombkowski, DrPH., MS, is Research Professor with the Child Health Evaluation and Research (CHEAR) Center within the University of Michigan Department of Pediatrics.   He is a health services researcher working extensively with public health information systems and large administrative claims databases.  

Kevin’s primary research focus is conducting population-based interventions aimed at improving the health of children, especially those with chronic conditions.  Much of his work has focused on evaluating the feasibility and accuracy of using administrative claims data to identify children with chronic conditions by linking these data with clinical and public health systems.  Many of these projects have linked claims, immunization registries, newborn screening, birth records and death records to conduct population-based evaluations of health services.  He has also applied these approaches to assess the statewide prevalence of chronic conditions such as asthma, sickle cell disease, and inflammatory bowel disease in Michigan as well as other states.  Kevin is currently collaborating with Michigan State University on the design and development of the Flint Lead Exposure Registry (FLExR) information architecture.

Kevin’s research interests also include registry-based interventions to improve the timeliness of vaccinations through automated reminder and recall systems.  He has led numerous collaborations with the Michigan Department of Health and Human Services (MDHHS), including several CDC-funded initiatives using the Michigan Care Improvement Registry (MCIR).  Through this collaboration, Kevin tested a statewide intervention aimed at increasing influenza vaccination among children with chronic conditions during the 2009 influenza pandemic.  Kevin is currently collaborating with MDHHS to evaluate MCIR data quality as immunization providers across Michigan adopt real-time, bi-directional messaging between electronic health records and MCIR.   He is conducting a similar statewide evaluation as new messaging protocols are adopted by electronic laboratory systems for reporting blood lead testing results to MDHHS.