title: DiMeX: A Text Mining System for Mutation-Disease Association Extraction.
abstract: The number of published articles describing associations between mutations and diseases is increasing at a fast pace. There is a pressing need to gather such mutation-disease associations into public knowledge bases, but manual curation slows down the growth of such databases. We have addressed this problem by developing a text-mining system (DiMeX) to extract mutation to disease associations from publication abstracts. DiMeX consists of a series of natural language processing modules that preprocess input text and apply syntactic and semantic patterns to extract mutation-disease associations. DiMeX achieves high precision and recall with F-scores of 0.88, 0.91 and 0.89 when evaluated on three different datasets for mutation-disease associations. DiMeX includes a separate component that extracts mutation mentions in text and associates them with genes. This component has been also evaluated on different datasets and shown to achieve state-of-the-art performance. The results indicate that our system outperforms the existing mutation-disease association tools, addressing the low precision problems suffered by most approaches. DiMeX was applied on a large set of abstracts from Medline to extract mutation-disease associations, as well as other relevant information including patient/cohort size and population data. The results are stored in a database that can be queried and downloaded at http://biotm.cis.udel.edu/dimex/. We conclude that this high-throughput text-mining approach has the potential to significantly assist researchers and curators to enrich mutation databases.
authors.affiliation: Department of Computer and Information Sciences, University of Delaware, Newark, Delaware, United States of America.
authors.forename: A S M Ashique
authors.initials: AS
authors.lastname: Mahmood
authors.affiliation: Department of Biochemistry and Molecular Medicine, George Washington University, Washington, District of Columbia, United States of America.
authors.forename: Tsung-Jung
authors.initials: TJ
authors.lastname: Wu
authors.affiliation: Department of Biochemistry and Molecular Medicine, George Washington University, Washington, District of Columbia, United States of America.|McCormick Genomic and Proteomic Center, George Washington University, Washington, District of Columbia, United States of America.
authors.forename: Raja
authors.initials: R
authors.lastname: Mazumder
authors.affiliation: Department of Computer and Information Sciences, University of Delaware, Newark, Delaware, United States of America.
authors.forename: K
authors.initials: K
authors.lastname: Vijay-Shanker
mesh_terms: D019295:Computational Biology / Q000379:methods; D057225:Data Mining / Q000379:methods*; D030541:Databases, Genetic; D020022:Genetic Predisposition to Disease; D006801:Humans; D051188:Knowledge Bases; D009154:Mutation; D009323:Natural Language Processing