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.