Document Type
Journal Article
Publication Date
1-1-2016
Journal
PLoS One
Volume
11
Issue
4
Inclusive Pages
Article number: e0152725
DOI
10.1371/journal.pone.0152725
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.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
APA Citation
Mahmood, A., Wu, T., Mazumder, R., & Vijay-Shanker, K. (2016). DiMeX: A Text Mining System for Mutation-Disease Association Extraction.. PLoS One, 11 (4). http://dx.doi.org/10.1371/journal.pone.0152725
Peer Reviewed
1
Open Access
1
Comments
Reproduced with permission of PLOS One.