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BioInformatics with Chad Myers and Rui Kuang

Date of Event: 
Saturday, October 6, 2018 - 8:00am to 11:00am
3-230 Keller Hall
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Presented by the University of Minnesota Software Engineering Center. All Software Engineering Industrial Seminars are open to the public.

This month we will have two presentations:

Title: Network-based machine learning and graph theory methods for cancer genomics

Speaker: Dr. Rui Kuang

Abstract: Network-based analytics plays an increasingly important role in precision oncology. Growing evidence in recent studies suggests that cancer can be better understood through mutated or dysregulated pathways or networks rather than individual mutations and that the efficacy of repositioned drugs can be inferred from disease modules in molecular networks. This talk reviews network-based machine learning and graph theory algorithms for integrative analysis of personal genomic data and biomedical knowledge bases to identify tumor-specific molecular mechanisms, candidate targets and repositioned drugs for personalized treatment. The review focuses on the algorithmic design and mathematical formulation of these methods to facilitate applications and implementations of network-based analysis in the practice of precision oncology. We review the methods applied in three scenarios to integrate genomic data and network models in different analysis pipelines, and we examine three categories of network-based approaches for repositioning drugs in drug–disease–gene networks. In addition, we perform a comprehensive subnetwork/pathway analysis of mutations in 31 cancer genome projects in the Cancer Genome Atlas and present a detailed case study on ovarian cancer. Finally, we discuss interesting observations, potential pitfalls and future directions in network-based precision oncology. Our article is available at

View video of Dr. Kuang's presentation on the UMSEC Youtube page.

Bio: Dr. Rui Kuang is an associate professor in computer science and engineering at the University of Minnesota Twin Cities. With training in computer science and bioinformatics, he is particularly interested in large-scale genomic and biomedical data analysis with machine learning and network-based methods for research problems in health and biological sciences. His lab develops algorithms and models for understanding the molecular characteristics of disease phenotypes from high-throughput sequencing data and biomedical knowledge bases. His current projects center around cancer biomarker identification, disease phenome-genome association analysis, and protein remote homology detection. Dr. Kuang is a recipient of NSF career award. He received his PhD from Columbia University in 2006, MS from Temple University in 2002 and BS from Nankai University in 1999, all in computer science.

Title: Mining human genomes for genetic interactions underlying disease

Speaker: Dr. Chad Myers

Abstract: The recent availability of genome sequences has enabled genome-wide association studies, which attempt to link specific genetic variants to disease. While these studies have produced a number of new candidate genetic loci, most still fail to explain the large majority of heritability associated with common diseases. One explanation is the presence of genetic interactions, or instances where multiple variants combine to cause disease. I will describe our recent efforts to develop computational methods to address this problem. Our work leverages a decade of experiments and data mining of the yeast model organism, where genome editing on a massive scale has been possible for many years. I will describe our efforts to translate insights about genetic interactions from this model system to develop new approaches for interpreting human genomes.

View video of Dr. Myers' presentation on the UMSEC Youtube page.

Bio: Chad Myers received his Ph.D. from the Department of Computer Science and the Lewis-Sigler Institute for Integrative Genomics at Princeton University in 2007. He is currently an Associate Professor in the Department of Computer Science and Engineering at the University of Minnesota. Dr. Myers’s research focuses on computational methods for analysis and interpretation of large-scale genetic interaction networks and methods for integration of diverse genomic data to predict gene function or infer biological networks. His lab is developing approaches for analyzing and leveraging interaction networks to answer biological questions in a variety of systems from yeast to humans.