title: Application of automatic mutation-gene pair extraction to diseases.
abstract: To have a better understanding of the mechanisms of disease development, knowledge of mutations and the genes on which the mutations occur is of crucial importance. Information on disease-related mutations can be accessed through public databases or biomedical literature sources. However, information retrieval from such resources can be problematic because of two reasons: manually created databases are usually incomplete and not up to date, and reading through a vast amount of publicly available biomedical documents is very time-consuming. In this paper, we describe an automated system, MuGeX (Mutation Gene eXtractor), that automatically extracts mutation-gene pairs from Medline abstracts for a disease query. Our system is tested on a corpus that consists of 231 Medline abstracts. While recall for mutation detection alone is 85.9%, precision is 95.9%. For extraction of mutation-gene pairs, we focus on Alzheimer's disease. The recall for mutation-gene pair identification is estimated at 91.3%, and precision is estimated at 88.9%. With automatic extraction techniques, MuGeX overcomes the problems of information retrieval from public resources and reduces the time required to access relevant information, while preserving the accuracy of retrieved information.
authors.affiliation: Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey. mugeerdogmus@su.sabanciuniv.edu
authors.forename: Muge
authors.initials: M
authors.lastname: Erdogmus
authors.forename: Osman Ugur
authors.initials: OU
authors.lastname: Sezerman
mesh_terms: D000043:Abstracting and Indexing; D000465:Algorithms; D000544:Alzheimer Disease / Q000235:genetics; D001499:Bayes Theorem; D019295:Computational Biology; D030541:Databases, Genetic; D004194:Disease; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods* / Q000706:statistics & numerical data; D016239:MEDLINE; D009154:Mutation; D012984:Software