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DiMeX: A Text Mining System for Mutation-Disease Association Extraction.
- Source :
- PLoS ONE; 4/13/2016, Vol. 11 Issue 4, p1-26, 26p
- Publication Year :
- 2016
-
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 . We conclude that this high-throughput text-mining approach has the potential to significantly assist researchers and curators to enrich mutation databases. [ABSTRACT FROM AUTHOR]
- Subjects :
- GENETIC mutation
NATURAL language processing
SEMANTICS
TEXT mining
MEDLINE
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 11
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- PLoS ONE
- Publication Type :
- Academic Journal
- Accession number :
- 114507009
- Full Text :
- https://doi.org/10.1371/journal.pone.0152725