Paper: Evaluating Joint Modeling of Yeast Biology Literature and Protein-Protein Interaction Networks

ACL ID W12-2419
Title Evaluating Joint Modeling of Yeast Biology Literature and Protein-Protein Interaction Networks
Venue Workshop on Biomedical Natural Language Processing
Session
Year 2012
Authors

Block-LDA is a topic modeling approach to perform data fusion between entity-annotated text documents and graphs with entity-entity links. We evaluate Block-LDA in the yeast bi- ology domain by jointly modeling PubMed R? articles and yeast protein-protein interaction networks. The topic coherence of the emer- gent topics and the ability of the model to re- trieve relevant scientific articles and proteins related to the topic are compared to that of a text-only approach that does not make use of the protein-protein interaction matrix. Eval- uation of the results by biologists show that the joint modeling results in better topic co- herence and improves retrieval performance in the task of identifying top related papers and proteins.