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MIA Talks

Primer: Learning biological patterns across domains: Investigating and integrating information across data types and sources

September 25, 2019
Goldenberg Lab, SickKids Research Institute; Dept. of Computer Science, University of Toronto; Vector Institute

In biomedical research, computational models are often used to infer biological knowledge from limited data (e.g. a given tissue, cell line, patient population, etc) with the intention of generalizing findings. In some cases the data can be successfully repurposed to answer a question it was not necessarily collected to answer, while in others, it falls short of its intended purpose. This talk will serve as a primer to Dr. Goldenberg’s discussion of prediction tasks across domains. First I will describe how we elucidate tissue-specific vs tissue-agnostic patterns of regulation using predictive models of gene expression across 21 tissues. From this work, we generate annotations which can be utilized for post-hoc analyses of regulator-phenotype associations. Then I will give a theoretical overview of domain adaptation and its application to the problem of patient drug-response prediction based on cell line data. Here I will focus on the assumptions of domain adaptation models and implications of these assumptions being violated.