Improving prediction accuracy using decision-tree-based meta-strategy and multi-threshold sequential-voting exemplified by miRNA target prediction

Lots of computational predictors have been developed for fast and large-scale analysis of biological data. However, many of them were developed long time ago when training datasets or sets of input features were rather small. Consequently, the utility of these predictors in much large datasets, which are very common in nowadays, need to be examined carefully. In addition, with the rapid development of scientific research, the expectation on the prediction accuracy of computational predictors is continuously uplifting. Therefore, developing novel strategies to improve the prediction accuracies of computational predictors becomes critical. In this study, the predictive results of existing individual miRNA target predictors were integrated into a decision-tree to make meta-prediction. When the multi-threshold sequential-voting technique was used, the prediction accuracy of the decision-tree was significantly improved by at least thirty percentage points compared to the individual predictors.