Distinguished Lecture in Causal Discovery on Thursday, October 22, 2015 by Jun Zhu, PhD, Icahn School of Medicine at Mount Sinai

 

Center for Causal Discovery

Distinguished Lecture in Causal Discovery

University of Pittsburgh, Carnegie Mellon University,

Pittsburgh Supercomputing Center and Yale University

 

Jun Zhu, PhD, Professor, Icahn Institute and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, will deliver the Distinguished Lecture in Causal Discovery, Integrative Causality Tests for Complex Diseases,” at 11:00 am on Thursday, October 22, 2015, in the Rooms 407A/B BAUM, 5607 Baum Blvd., The Offices at Baum.

 

Abstract:  Cells employ multiple levels of regulation, including transcriptional and translational regulation that drive core biological processes and enable cells to dynamically respond to genetic and environmental changes.   A plethora of diverse high throughput data has been generated to measure different aspects of biological regulations in quasi static states or time series.  To achieve comprehensive understanding of these complex biological processes, we developed multiple methods to integrate diverse types of data such as genetic and epigenetic variation, RNA expression data, and metabolomics data.  We extended the framework to integrate many data types to construct probabilistic causal networks that elucidate the complexity of cell regulation.   The goals of our integrative analysis are not only to find causal regulators, but to uncover mechanisms by which these predicted causal regulators affect complex disease phenotypes.

 

Biography:  Jun Zhu, PhD is a professor in Department of Genetics and Genomic Sciences and a member of Institute of Genomics and Multiscale Biology.  He received his M.S and Ph.D. at State University of New York at Albany.  Dr. Zhu was trained in both computer science and biomedical science.    His research focuses on methodologies for integrating diverse data (e.g. genetic, genomic, transcriptomic, proteomic data and etc.) into probabilistic graphic networks for elucidating complex human diseases, as well as method development for analyzing and comparing disease-related networks.  Before joining Mount Sinai, he worked in both industry (Amgen, Inc, and Rosetta/Merck & CO, Inc) and academia (Sage Bionetworks, and Fred Hutchinson Cancer Research Center).

Post Date: 
Wednesday, October 7, 2015
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