DiMeX: A Text Mining System for Mutation- Disease Association Extraction
| Author(s) | Mahmood, A. S. M. Ashique | |
| Author(s) | Wu, Tsung-Jung | |
| Author(s) | Mazumder, Raja | |
| Author(s) | Vijay-Shanker, K. | |
| Ordered Author | A. S. M. Ashique Mahmood, Tsung-Jung Wu, Raja Mazumder, K. Vijay-Shanker | |
| UD Author | Mahmood, A. S. M. Ashique | en_US |
| UD Author | Vijay-Shanker, K. | en_US |
| Date Accessioned | 2016-11-10T15:56:09Z | |
| Date Available | 2016-11-10T15:56:09Z | |
| Copyright Date | Copyright © 2016 Mahmood et al. | en_US |
| Publication Date | 2016-04-13 | |
| Description | Publisher's PDF | en_US |
| 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. | en_US |
| Department | University of Delaware. Department of Computer and Information Sciences. | en_US |
| Citation | 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 | en_US |
| DOI | doi:10.1371/journal. pone.0152725 | en_US |
| ISSN | 1932-6203 | en_US |
| URL | http://udspace.udel.edu/handle/19716/19832 | |
| Language | en_US | en_US |
| Publisher | Public Library of Science | en_US |
| dc.rights | CC BY | en_US |
| dc.source | PLOS One | en_US |
| dc.source.uri | http://journals.plos.org/plosone/ | en_US |
| Title | DiMeX: A Text Mining System for Mutation- Disease Association Extraction | en_US |
| Type | Article | en_US |
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