DiMeX: A Text Mining System for Mutation- Disease Association Extraction
Date
2016-04-13
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Publisher
Public Library of Science
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.
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Mahmood ASMA, Wu T-J, Mazumder R, Vijay-Shanker K (2016) DiMeX: A Text Mining System for Mutation-Disease Association Extraction. PLoS ONE 11(4): e0152725. doi:10.1371/journal. pone.0152725