Mark Craven is a professor in the Department of Biostatistics and Medical Informatics at the University of Wisconsin, and an affiliate faculty member in the Department of Computer Sciences. He is the Director of the Center for Predictive Computational Phenotyping, one of the NIH Centers of Excellence for Big Data Computing. He is also the Director of the NIH/NLM-funded Computation and Informatics in Biology and Medicine (CIBM) Training Program, and a member of the Institute for Clinical and Translational Research, the Carbone Cancer Center, and the Genome Center of Wisconsin. The focus of his research program is on developing and applying machine-learning methods to infer models of, and reason about, networks of interactions among genes, proteins, clinical and environmental factors, and phenotypes of interest.
Inferring Host-Virus Interactions from Diverse Data Sources
Insight into the mechanisms and context of host-virus interactions can be gained by applying
computational methods to a broad range of experimental, observational, and secondary data
sources. I will discuss our work in several studies that involve developing and applying
predictive methods in order to characterize host-virus interactions. These studies incorporate
viral genomic sequences, genome-wide loss-of-function screens, disease phenotypes measured
in hosts, the scientific literature, and electronic health records as data sources.
Dr. Huang is an Associate Professor at the Department of Experimental and Clinical Pharmacology, University of Minnesota. She is also an Associate Director for the Institute of Personalized Medicine | Pharmacogenomics U of M Alliance (PUMA-IPM) and a member of the Masonic Cancer Center at the University of Minnesota. She is a member of American Association for Cancer Research (AACR), American Society of Human Genetics (ASHG), and American Society of Clinical Pharmacology and Therapeutics (ASCPT). To date, she has published over 70 original research papers many of which are in high caliber journals, e.g., Nature, Nature Medicine, PNAS, Blood, Cancer Research, Genome Biology and American Journal of Human Genetics. Dr. Huang is a board certified clinical pharmacologist with extensive training in genetics, molecular and cell biology, clinical trials and high throughput data analysis.
The Huang laboratory’s main research focus is translational pharmacogenomics with particular interest in the pharmacogenomics of anti-cancer agents. By systematically evaluating human genome and its relationships to drug response and toxicity, their goal is to develop clinically useful models that predict risks for adverse drug reactions and non-response prior to administration of chemotherapy. With her broad training background, Dr. Huang assembles and leads a multi-disciplinary team that consists of computational biologist, geneticist, pharmacist, physician, molecular biologist and biostatistician to tackle a series of serious problems in cancer research. These include the lack of mechanistic understanding of genomic regulation of cancer phenotypes; the lack of reproducible predictive biomarkers for cancer therapeutic agents; and the lack of effective treatment for many hard to treat cancers.
More information about the Huang lab can be found online at http://huang-lab.umn.edu/
Bridging Pre-Clinical Drug Screening with Patient Molecular Profiles for Biomarker Discovery and Drug Repurposing
Using computational methods developed in our lab, we imputed drug response in very large clinical cancer genomics data sets, such as The Cancer Genome Atlas (TCGA). This yields a new resource of imputed drug response for every drug in each patient. These imputed drug response data are then used for biomarker identification through association analysis with various molecular markers measured in the clinical cohort; and/or for drug repurposing.
Hae Kyung Im develops statistical methods to make sense of large amounts of genomic and other high dimensional data with the ultimate goal of making discoveries that can be translated into improving human health. Her current focus is in the integration of genome wide association and functional genomics studies and prediction of complex traits to understand the etiology of complex diseases and traits. She has conceived and developed the widely used PrediXcan framework, which tests the mediating effects of gene expression traits on phenotypes. After trying out physics, manufacturing, information security, and finance, she found a home in academia in the intersection of statistics, genomics, medicine, and big data analytics. She is currently an Assistant Professor in the Section of Genetic Medicine at the University of Chicago.
Leveraging Large Scale Genome and Transcriptome to Decode the Biology of Complex Traits
Over the last decade and half, the field of complex trait genetics has made unprecedented amount of progress discovering tens of thousands of variants robustly associated with a broad spectrum of human diseases and traits. Despite this success, the understanding of the mechanisms underlying these discoveries is lagging. In this talk, I will survey what we have learned about the biology of complex traits using genome-wide association studies, biobank-level phenome data, comprehensive atlas of transcriptome regulation, and a suite of methods tailored to integrate these data.
Data-enabled integrative analysis for fine mapping and interpretation of associated genetic variants
While there has been many advances in incorporating prior information into prioritization of associated variants in genome-wide and molecular association studies, functional annotation data rarely played more than an indirect role in assessing evidence for association in these approaches. This talk is organized around our recent work on generating large-scale annotation data for single nucleotide variants and their model-based integration into fine mapping of quantitative and molecular traits.
Dr. Dan Knights is a computational microbiologist. He is a professor in the Department of Computer Science and Engineering and the Biotechnology Institute at the University of Minnesota. Dan received his B.A. from Middlebury College, and his PhD from the University of Colorado, both in Computer Science and Computational Biology, followed by a post-doctoral fellowship at Harvard. His research uses data mining and machine learning to link microbial and human genomic data to human disease. Dan has co-authored articles in top multidisciplinary journals. In 2015 he was named a McKnight Land-Grant Professor by the University of Minnesota.
Less is More: Extracting features from shallow sequencing data
Microbiomes are complex and highly variable, requiring analysis of massive quantities of microbial DNA from biological samples. Unfortunately, clinical microbiome researchers often have to choose between having high-resolution data, via deep shotgun sequencing, or having larger sample sizes, via affordable but low-resolution marker gene sequencing. Using real examples in clinical microbiome studies, this talk discusses methods for increasing power using larger studies with shallow shotgun metagenomics sequencing.
Reading the fossil record of a cancer
During carcinogenesis, cells accumulate 1000s of somatic DNA mutations. Driver mutations bestow fitness advantages that lead to selective sweeps that increase that frequency of mutated cells compared to those lacking the driver. These sweeps also increase the frequency of passenger mutations accumulated since the last such sweep. These mutation "fossils" have little impact on cell function but reflect the mutational processes that generated them. Both their type (i.e., A to C) and genomic locations depend on both what caused the mutation, e.g., UV light, and also the chromatin state of the cell that acquired it. I will describe machine learning approaches to (i) group mutations into subclones associated with different sweeps, (ii) reconstruct the phylogenies of these subclones, and (iii) to analyze these groups to infer properties of the historical cell environment in which these mutations accumulated. The ultimate goal of this work is to reconstruct the dynamic cell environments as a normal cell progressively transforms into a cancerous one.