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

Paper:Jeongkyun Kim, Jung-jae Kim, Hyunju Lee* (2019) DigChem: Identification of disease-gene-chemical relationships from Medline abstracts. PLoS Computational Biology, In press.<br>Introduction: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.<br>Description:DigChem is available at http://gcancer.org/digchem. The unique triplets identified from DigChem can be downloaded from here.

论文：Jeongkyun Kim、Jung-jae Kim、Hyunju Lee*（2019）《DigChem：从Medline医学文献摘要中识别疾病-基因-化学物关联》，刊载于《PLoS计算生物学》（PLoS Computational Biology），即将刊出。<br>研究介绍：本研究提出了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医学文献摘要中识别基因、化学物与疾病之间关联的佐证语句。随后，我们开发了搜索引擎DigChem，可针对35,124个基因、56,382种化学物及5,675种疾病提供疾病-基因-化学物关联检索服务。通过与人工审校结果及现有数据库进行比对，我们验证了所识别关联的可靠性。<br>数据集说明：DigChem的访问地址为http://gcancer.org/digchem。从DigChem中提取得到的唯一关联三元组可通过此处下载。