Relation Extraction for Protein-protein Interactions Affected by Mutations

Precision Medicine has attracted increasing attention from biomedical research. Extracting information from biomedical literature about protein-protein interactions affected by mutations is a vital step towards PM because it uncovers mechanisms leading to diseases. We investigate a feature-rich supervised method to accomplish this relation extraction challenge. Our approach leverages a novel combination of features, as well as two auxiliary corpora, to achieve up 44% improvement in F1-score over baseline method.