MicroRNA target prediction based on second-order Hidden Markov Model  

作　　者：Song GAO Liangsheng ZHANG Diangang QIN Tienan FENG Yifei WANG 

机构地区：[1]Department of Mathematics,School of Sciences,Shanghai University,Shanghai 200444,China [2]School of Life Sciences,Institute of Plant Biology,Fudan University,Shanghai 200433,China

出　　处：《Frontiers in Biology》2010年第2期171-179,共9页生物学前沿（英文版）

基　　金：This work is supported by The National Natural Science Foundation of China(Grant No.30871341);the grants from the National Key S&T Special Project of China(Nos.2008ZX10002-017,2008ZX10002-020,and 2009ZX09103-686);Shanghai Key Discipline of China(No.S30104);Education Commission Key Discipline Construction Project(No.J50101).

摘　　要：MicroRNAs are one class of small singlestranded RNA of about 22 nt serving as important negative gene regulators.In animals,miRNAs mainly repress protein translation by binding itself to the 3'UTR regions of mRNAs with imperfect complementary pairing.Although bioinformatics investigations have resulted in a number of target prediction tools,all of these have a common shortcoming—a high false positive rate.Therefore,it is important to further filter the predicted targets.In this paper,based on miRNA:target duplex,we construct a second-order Hidden Markov Model,implement Baum-Welch training algorithm and apply this model to further process predicted targets.The model trains the classifier by 244 positive and 49 negative miRNA:target interaction pairs and achieves a sensitivity of 72.54%,specificity of 55.10%and accuracy of 69.62%by 10-fold crossvalidation experiments.In order to further verify the applicability of the algorithm,previously collected datasets,including 195 positive and 38 negative,are chosen to test it,with consistent results.We believe that our method will provide some guidance for experimental biologists,especially in choosing miRNA targets for validation.

关 键 词：MICRORNA target gene experimentally supported targets second-order Hidden Markov Model forward algorithm 

分 类 号：R73[医药卫生—肿瘤]