title: Approximate subgraph matching-based literature mining for biomedical events and relations.
abstract: The biomedical text mining community has focused on developing techniques to automatically extract important relations between biological components and semantic events involving genes or proteins from literature. In this paper, we propose a novel approach for mining relations and events in the biomedical literature using approximate subgraph matching. Extraction of such knowledge is performed by searching for an approximate subgraph isomorphism between key contextual dependencies and input sentence graphs. Our approach significantly increases the chance of retrieving relations or events encoded within complex dependency contexts by introducing error tolerance into the graph matching process, while maintaining the extraction precision at a high level. When evaluated on practical tasks, it achieves a 51.12% F-score in extracting nine types of biological events on the GE task of the BioNLP-ST 2011 and an 84.22% F-score in detecting protein-residue associations. The performance is comparable to the reported systems across these tasks, and thus demonstrates the generalizability of our proposed approach.
authors.affiliation: National Center for Biotechnology Information, Bethesda, Maryland, United States of America. Haibin.Liu@nih.gov
authors.forename: Haibin
authors.initials: H
authors.lastname: Liu
authors.forename: Lawrence
authors.initials: L
authors.lastname: Hunter
authors.forename: Vlado
authors.initials: V
authors.lastname: Kešelj
authors.forename: Karin
authors.initials: K
authors.lastname: Verspoor
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000378:metabolism; D020811:Biomedical Technology; D057225:Data Mining / Q000379:methods*; D030562:Databases, Protein; D011506:Proteins / Q000378:metabolism; D011642:Publications; D015203:Reproducibility of Results
chemical_list: D000596:Amino Acids; D011506:Proteins