DeepM6ASeq-EL:prediction of human N6-methyladenosine(m^(6)A)sites with LSTM and ensemble learning  

作　　者：Juntao CHEN Quan ZOU Jing LI 

机构地区：[1]Institute of Fundamental and Frontier Sciences,University of Electronic Science and Technology of China,Chengdu 610051,China [2]Hainan Key Laboratory for Computational Science and Application,Hainan Normal University,Haikou 571158,China [3]College of Intelligence and Computing,Tianjin University,Tianjin 300350,China

出　　处：《Frontiers of Computer Science》2022年第2期27-33,共7页中国计算机科学前沿（英文版）

基　　金：The work was supported by the National Natural Science Foundation of China(Grant Nos.61922020,61771331,91935302).

摘　　要：N6-methyladenosine(m^(6)A)is a prevalent methylation modification and plays a vital role in various biological processes,such as metabolism,mRNA processing,synthesis,and transport.Recent studies have suggested that m^(6)A modification is related to common diseases such as cancer,tumours,and obesity.Therefore,accurate prediction of methylation sites in RNA sequences has emerged as a critical issue in the area of bioinformatics.However,traditional high-throughput sequencing and wet bench experimental techniques have the disadvantages of high costs,significant time requirements and inaccurate identification of sites.But through the use of traditional experimental methods,researchers have produced many large databases of m^(6)A sites.With the support of these basic databases and existing deep learning methods,we developed an m^(6)A site predictor named DeepM6ASeq-EL,which integrates an ensemble of five LSTM and CNN classifiers with the combined strategy of hard voting.Compared to the state-of-the-art prediction method WHISTLE(average AUC 0.948 and 0.880),the DeepM6ASeq-EL had a lower accuracy in m^(6)A site prediction(average AUC:0.861 for the full transcript models and 0.809 for the mature messenger RNA models)when tested on six independent datasets.

关 键 词：N6-methyladenosine site prediction LSTM CNN ensemble learning 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]