DigChem: Identification of disease-gene-chemical relationships from Medline abstracts.

Autor: Jeongkyun Kim, Jung-Jae Kim, Hyunju Lee
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: PLoS Computational Biology, Vol 15, Iss 5, p e1007022 (2019)
Druh dokumentu: article
ISSN: 1553-734X
1553-7358
DOI: 10.1371/journal.pcbi.1007022
Popis: Chemicals interact with genes in the process of disease development and treatment. Although much biomedical research has been performed to understand relationships among genes, chemicals, and diseases, which have been reported in biomedical articles in Medline, there are few studies that extract disease-gene-chemical relationships from biomedical literature at a PubMed scale. In this study, we propose a deep learning model based on bidirectional long short-term memory to identify the evidence sentences of relationships among genes, chemicals, and diseases from Medline abstracts. Then, we develop the search engine DigChem to enable disease-gene-chemical relationship searches for 35,124 genes, 56,382 chemicals, and 5,675 diseases. We show that the identified relationships are reliable by comparing them with manual curation and existing databases. DigChem is available at http://gcancer.org/digchem.
Databáze: Directory of Open Access Journals
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