OREGON STATE UNIVERSITY

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Using Machine Learning to Understand Gene Regulation

Monday, January 30, 2017 -
4:00pm to 4:50pm
GLFN AUD (Gilfillan Auditorium)

Speaker Information

Molly Megraw
Assistant Prof.
Department of Botany and Plant Pathology
Oregon State University

Abstract

My laboratory is broadly interested in understanding how certain important small RNAs known as “microRNAs” and important protein-coding genes known as “Transcription Factors” work together in living cells.  As a part of these studies, we need to identify (1) which RNAs and genes interact, and (2) which interactions form circuits that play key physiological roles within specific tissues of an organism.  Our recent work in these areas has given rise to two challenges which may interest EECS students, postdocs, or other collaborators.  In the first portion of the talk I will demonstrate how a machine learning model can suggest sets of gene interactions which have the potential to “turn on” a particular gene, and briefly discuss one possible approach for dissecting which of those sets are optimal predictors of gene up-regulation.  In the second portion of the talk I will present a new project that seeks to predict the tissue in which a given gene will express.  At the end of the talk I will briefly present a new course offering for Spring 2017 that is designed to introduce concepts in Genome Biology to students from EECS who would like to explore computational biology as an application area but have never taken a biology class before.

Speaker Bio

Molly Megraw received her doctoral degree in Genomics and Computational Biology from the University of Pennsylvania.  During her post-doctoral work at Duke University, she developed a machine learning model which demonstrates that highly accurate gene and microRNA transcription start site prediction can be achieved using DNA sequence information alone.  Her current work combines computational analysis of gene regulatory network topology with experimental methods for Transcription Start Site Sequencing library generation to identify gene regulatory circuits in multiple tissues of the Arabidopsis thaliana model plant system.  In 2012 she began a faculty position in Systems Biology within the Center for Genome Research and Biocomputing at Oregon State University, the departmental home for her laboratory is Botany & Plant Pathology.