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Tuesday, October 15 • 11:30am - 11:45am
Composed Relation-Based Learning (CoRL): Predictive Modeling of Drug Side-Effect Relationships

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Drug side-effects are unfortunately common and are often undetected until after a drug has been released. The current state of the art relies on observing a disproportionate co-occurrence of a drug with a potential side-effect for detection. This methodology does not consider relational or causal information, or similarity between drugs and side-effects for classification. In this work, embeddings are learned from literature-derived relational connections, and are entangled together for pairs of interest and leveraged with supervised machine learning. This composed, relation-based learning (CoRL) produces state of the art performance on two widely used, manually curated reference standards for drug safety monitoring.


Justin Mower

Presenter, Rice University

Trevor Cohen

University of Washington

Devika Subramanian

Rice University
My research interests are in artificial intelligence and machine learning and their applications in computational systems biology, neuroscience of human learning, assessments of hurricane risks, network analysis of power grids, mortality prediction in cardiology, conflict forecasting... Read More →

Tuesday October 15, 2019 11:30am - 11:45am CDT
BRC 280

Attendees (4)