Tissue specific prediction of N^(6)-methyladenine sites based on an ensemble of multi-input hybrid neural network  

作　　者：CANGZHI JIA DONG JIN XIN WANG QI ZHAO 

机构地区：[1]School of Science,Dalian Maritime University,Dalian,116026,China [2]School of Computer Science and Software Engineering,University of Science and Technology Liaoning,Anshan,114051,China

出　　处：《BIOCELL》2022年第4期1105-1121,共17页生物细胞（英文）

基　　金：supported by the National Natural Science Foundation of China(Nos.62071079 and 61803065).

摘　　要：N^(6)-Methyladenine is a dynamic and reversible post translational modification,which plays an essential role in various biological processes.Because of the current inability to identify m6A-containing mRNAs,computational approaches have been developed to identify m6A sites in DNA sequences.Aiming to improve prediction performance,we introduced a novel ensemble computational approach based on three hybrid deep neural networks,including a convolutional neural network,a capsule network,and a bidirectional gated recurrent unit(BiGRU)with the self-attention mechanism,to identify m6A sites in four tissues of three species.Across a total of 11 datasets,we selected different feature subsets,after optimized from 4933 dimensional features,as input for the deep hybrid neural networks.In addition,to solve the deviation caused by the relatively small number of experimentally verified samples,we constructed an ensemble model through integrating five sub-classifiers based on different training datasets.When compared through 5-fold cross-validation and independent tests,our model showed its superiority to previous methods,im6A-TS-CNN and iRNA-m6A.

关 键 词：M6A sites Deep hybrid neural networks Ensemble model Feature selection 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]