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

by Jeongkyun Kim, Jung-jae Kim, Hyunju Lee

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.

Tratto da: www.plos.org
Note sul Copyright: Articles and accompanying materials published by PLOS on the PLOS Sites, unless otherwise indicated, are licensed by the respective authors of such articles for use and distribution by you subject to citation of the original source in accordance with the Creative Commons Attribution (CC BY) license.