title: MiRTDL: A Deep Learning Approach for miRNA Target Prediction.
abstract: MicroRNAs (miRNAs) regulate genes that are associated with various diseases. To better understand miRNAs, the miRNA regulatory mechanism needs to be investigated and the real targets identified. Here, we present miRTDL, a new miRNA target prediction algorithm based on convolutional neural network (CNN). The CNN automatically extracts essential information from the input data rather than completely relying on the input dataset generated artificially when the precise miRNA target mechanisms are poorly known. In this work, the constraint relaxing method is first used to construct a balanced training dataset to avoid inaccurate predictions caused by the existing unbalanced dataset. The miRTDL is then applied to 1,606 experimentally validated miRNA target pairs. Finally, the results show that our miRTDL outperforms the existing target prediction algorithms and achieves significantly higher sensitivity, specificity and accuracy of 88.43, 96.44, and 89.98 percent, respectively. We also investigate the miRNA target mechanism, and the results show that the complementation features are more important than the others.
authors.lastname: Shuang Cheng
authors.lastname: Maozu Guo
authors.lastname: Chunyu Wang
authors.lastname: Xiaoyan Liu
authors.lastname: Yang Liu
authors.lastname: Xuejian Wu
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D035683:MicroRNAs / Q000235:genetics*; D017423:Sequence Analysis, RNA / Q000379:methods*
chemical_list: D035683:MicroRNAs