title: Predicting human microRNA-disease associations based on support vector machine.
abstract: The identification of disease-related microRNAs is vital for understanding the pathogenesis of disease at the molecular level and may lead to the design of specific molecular tools for diagnosis, treatment and prevention. Experimental identification of disease-related microRNAs poses difficulties. Computational prediction of microRNA-disease associations is one of the complementary means. However, one major issue in microRNA studies is the lack of bioinformatics programs to accurately predict microRNA-disease associations. Herein, we present a machine-learning-based approach for distinguishing positive microRNA-disease associations from negative microRNA-disease associations. A set of features was extracted for each positive and negative microRNA-disease association, and a Support Vector Machine (SVM) classifier was trained, which achieved the area under the ROC curve of up to 0.8884 in 10-fold cross-validation procedure, indicating that the SVM-based approach described here can be used to predict potential microRNA-disease associations and formulate testable hypotheses to guide future biological experiments.
authors.affiliation: Academy of Fundamental and Interdisciplinary Sciences, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China. qhjiang@hit.edu.cn
authors.forename: Qinghua
authors.initials: Q
authors.lastname: Jiang
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China.
authors.forename: Guohua
authors.initials: G
authors.lastname: Wang
authors.affiliation: Department of Mathematics, Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin,
authors.forename: Shuilin
authors.initials: S
authors.lastname: Jin
authors.affiliation: Academy of Fundamental and Interdisciplinary Sciences, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China.
authors.forename: Yu
authors.initials: Y
authors.lastname: Li
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China.
authors.forename: Yadong
authors.initials: Y
authors.lastname: Wang
mesh_terms: D004194:Disease / Q000235:genetics; D006801:Humans; D035683:MicroRNAs / Q000737:chemistry*; D012372:ROC Curve; D060388:Support Vector Machine
chemical_list: D035683:MicroRNAs