Title: Predicting human microRNA-disease associations based on support vector machine

Authors: Qinghua Jiang; Guohua Wang; Shuilin Jin; Yu Li; Yadong Wang

Addresses: Academy of Fundamental and Interdisciplinary Sciences, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China ' School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China ' Department of Mathematics, Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China ' Academy of Fundamental and Interdisciplinary Sciences, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China ' School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China

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.

Keywords: microRNA-disease association prediction; SVM; support vector machines; microRNA; bioinformatics; machine learning; human diseases.

DOI: 10.1504/IJDMB.2013.056078

International Journal of Data Mining and Bioinformatics, 2013 Vol.8 No.3, pp.282 - 293

Received: 04 May 2011
Accepted: 04 May 2011
Published online: 20 Oct 2014 *

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