CRF-LSTM Text Mining Method Unveiling the Pharmacological Mechanism of Off-target Side Effect of Anti-Multiple Myeloma Drugs
Kaiyin Zhou, Sheng Zhang, Xiangyu Meng, Qi Luo, Yuxing Wang, Ke Ding, Yukun Feng, Mo Chen, Kevin Bretonnel Cohen, Jingbo Xia · 2018
Off-target effects played a vital role in the pharmacological understanding of drug efficacy and this research aimed to use text mining strategy to curate molecular level information and unveil the mechanism of off-target effect caused by the usage of anti-multiple myeloma (MM) drugs.After training a hybrid CNN-CRF-LSTM neural network upon the training data from TAC 2017 benchmark database, we extracted all of the side effects of 16 anti-MM drugs from drug labels, and combined the results with existed database.Afterwards, gene targets of anti-MM drugs were obtained by using structure similarity, and their related phenotypes were retrieved from Human Phenotype Ontology.Furthermore, linked phenotypes to candidate genes and adverse reaction of known drugs formed a knowledge graph.Through regulation analysis upon intersected phenotypes of drugs and target genes, an off-target effect caused by SLC7A7 was found, which with high possibility unveiled the pharmacological mechanism of side effect after using combination of anti-MM drugs.