Evaluating joint modeling of yeast biology literature and protein-protein interaction networks

Authors:
Ramnath Balasubramanyan;Kathryn Rivard;William W. Cohen;Jelena Jakovljevic;John Woolford
Affiliations:
Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University
Venue:
BioNLP '12 Proceedings of the 2012 Workshop on Biomedical Natural Language Processing
Year:
2012

Citing 3
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Abstract

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 biology domain by jointly modeling PubMed® articles and yeast protein-protein interaction networks. The topic coherence of the emergent topics and the ability of the model to retrieve 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. Evaluation of the results by biologists show that the joint modeling results in better topic coherence and improves retrieval performance in the task of identifying top related papers and proteins.