University of Pittsburgh

Pathway-Level Information ExtractoR (PLIER): a generative model for gene expression data

Assistant Professor
Friday, February 2, 2018 - 12:30pm - 1:30pm

The increasing ease of collecting genome-scale data has rapidly accelerated its use in all areas of biomedical science. Translating genome scale data in to testable hypothesis, on the other hand, is challenging and remains an active area method development. In this talk we present a machine learning aproach to produce data representations guided by a mechanistic understanding of the data generating process. Our method is a new constrained matrix decomposition approach that directly aligns a lower dimension representation with known biological pathways. The approach provides state-of-the-art accuracy in reconstructing known upstream variables and yields new insights into the archetecture of genetic regulation.

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