Abstract
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.
Highlights
ObjectivesSentence 2: “OBJECTIVE: The objective of this article is to test the effects of angiotensinconverting enzyme (ACE)-inhibition on glomerular epithelial cell number in an inducible experimental model of focal segmental glomerulosclerosis (FSGS).”
Much biomedical research has been performed to understand relationships among genes, chemicals, and diseases
Since the statistical measurements for inference often fail, we develop a deep learning model to identify the evidence sentences of disease–gene–chemical relationships from Medline abstracts
Summary
Sentence 2: “OBJECTIVE: The objective of this article is to test the effects of angiotensinconverting enzyme (ACE)-inhibition on glomerular epithelial cell number in an inducible experimental model of focal segmental glomerulosclerosis (FSGS).”. Our goal is to collect pairs of two sentences, one with gene-chemical relationship and another with a disease related to the gene-chemical relationship
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