Constructing a Gene-Drug-Adverse Reactions Network and Inferring Potential Gene-Adverse Reactions Associations Using a Text Mining Approach
Our objective was to identify and extract gene-drug and drugadverse drug reaction (ADR) relationships from different biomedical literature collections, and to predict the possible association between gene and ADR. The drug, ADR and gene entities were recognized by a CRF model with multiple features. Logistic regression models were constructed for each drug- ADR and drug-gene pair based on its frequency, Mesh Rule association and similarity with known association etc. Using predicted score to generate drug- ADR matrix and drug-gene matrix, and then calculating for gene-ADR matrix. Network and clustering analysis were applied to verify and interpret the relationship between them. A total of 78014 potential gene-ADR associations were predicted. Part of the predicted results can be explained by the network-clustering-pathway analysis, and verified in the literature. The gene-drug-ADR network constructed in this study can provide a reference for the possible association between the gene and ADR.
Drug-Related Side Effects and Adverse Reactions Data Mining Algorithms
Sui MingShuang Cui Lei
School of Medical Informatics,China Medical University,Shenyang,Liaoning,China
国际会议
第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)
苏州
英文
531-535
2017-08-21（万方平台首次上网日期，不代表论文的发表时间）