Kernel methods for predicting protein--protein interactions
Bioinformatics
Discovering breast cancer drug candidates from biomedical literature
International Journal of Data Mining and Bioinformatics
Predicting disease phenotypes based on the molecular networks with Condition-Responsive Correlation
International Journal of Data Mining and Bioinformatics
The Nature Of Statistical Learning Theory~
IEEE Transactions on Neural Networks
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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.