Prediction and Analysis of Mature microRNA with Flexible Neural Tree Model

miRNA is a class of small non-coding RNA molecules, length of about 20–24 nucleotides. It combines with mRNA by the principle of complementary base pairing to achieve the objective of cracking or suppressing mRNA, which has the function of gene regulation. Therefore, study on the prediction of miRNA is always the hot topic in bioinformatics. In this paper, we drew on a new method of feature extraction and combined the flexible neural tree (FNT) to predict miRNA. For comparison, we adopted XUE dataset, used the training dataset to train the classifier, and then used the classifier to test on testing dataset. The final average accuracy rate of our experiment that is 93.7% is higher than the prediction method of XUE triple-SVM. So our method achieves a better classification effect.

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