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

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.在疾病发生发展与治疗过程中，化学物质与基因存在相互作用。尽管学界已开展大量生物医学研究以解析基因、化学物质与疾病三者间的关联，且相关成果已刊载于Medline收录的生物医学期刊论文中，但目前鲜有研究能在与PubMed文献规模相当的前提下，从生物医学文献中提取疾病-基因-化学物质三者间的关联关系。本研究提出一种基于双向长短期记忆网络（bidirectional long short-term memory）的深度学习模型，用于从Medline收录的论文摘要中识别基因、化学物质与疾病三者关联的证据句子。随后，本研究开发了搜索引擎DigChem，可针对35124个基因、56382种化学物质以及5675种疾病，提供疾病-基因-化学物质关联关系的检索服务。通过与人工编审结果以及现有数据库进行比对验证，本研究证实所识别的关联关系具备可靠性。DigChem可通过网址http://gcancer.org/digchem公开访问。