Text Mining and Predicting Disease-Gene-Drug Associations of Hypertension Data Cubes-Based

Background Predispositions to hypertension is possibly associated with numerous potential gene polymorphisms and systemic disorders. Large-scale text mining of biomedical literature is a flexible and essential tool that can be applied to search for innovative drugs and treatments for diseases, such as investigating and predicting the bio-entities associations.Result We proposed a generality approach for extracting and predicting hypertension-related disease-gene-drug associations based on dictionary and data cube from biomedical abstracts. After data preprocessing, we constructed the 0-D vertex cube, which we then filtered to construct three 1-D cubes consisting of 252 diseases, 185 genes, and 141 drugs. By applying association rules to quantify the disease-gene-drug associations, we found 235 associations between 79 diseases and the 71 genes, and AUCs was 84.1%; 196 associations between 43 diseases and 102 drugs, and AUCs was 85.8%; 160 associations between 31 genes and 106 drugs, and AUCs was 83.6%. Using the bottom-up computation algorithm, we established three 2-D cubes and one 3-D disease-gene-drug cube, which revealed 591 associations between 90 diseases, 82 genes, and 145 drugs. Based on this 3-D cube, we obtained 262 predictive bio-entity association pairs of which 57 disease-drugs, 84 disease-genes, and 121 gene-drugs.Conclusions We have implemented and validated a data cube-based text mining approach to identifying and ranking the hypertension-related disease-gene-drug associations. Our results provide new pathways in the search for the potential treatment drugs of hypertension.