Using Machine Learning to Understand Gene Regulation GILB 124 Mon, 01/30/2017 - 4:00pm
Molly Megraw Assistant Prof., Department of Botany and Plant Pathology, Oregon State University Abstract: <p>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.</p> Bio: URL: http://eecs.oregonstate.edu/colloquium/using-machine-learning-understand... [1] [1] http://eecs.oregonstate.edu/colloquium/using-machine-learning-understand-gene-regulation
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