典型文献
Imputing DNA Methylation by Transferred Learning Based Neural Network
文献摘要：
DNA methylation is one important epigenetic type to play a vital role in many diseases including cancers.With the development of the high-throughput sequencing technology,there is much progress to disclose the relations of DNA methylation with diseases.However,the analyses of DNA methylation data are challenging due to the missing values caused by the limitations of current techniques.While many methods have been developed to impute the missing values,these methods are mostly based on the correlations between individual samples,and thus are limited for the abnormal samples in cancers.In this study,we present a novel transfer learning based neural network to impute missing DNA methylation data,namely the TDimpute-DNAmeth method.The method learns common relations between DNA methylation from pan-cancer samples,and then fine-tunes the learned relations over each specific cancer type for imputing the missing data.Tested on 16 cancer datasets,our method was shown to outperform other commonly-used methods.Further analyses indicated that DNA methylation is related to cancer survival and thus can be used as a biomarker of cancer prognosis.
文献关键词：
中图分类号：
作者姓名：
Xin-Feng Wang;Xiang Zhou;Jia-Hua Rao;Zhu-Jin Zhang;Yue-Dong Yang
作者机构：
School of Computer Science and Engineering,Sun Yat-sen University,Guangzhou 510000,China;Key Laboratory of Machine Intelligence and Advanced Computing of Ministry of Education(Sun Yat-sen University)Guangzhou 510000,China
文献出处：
引用格式：
[1]Xin-Feng Wang;Xiang Zhou;Jia-Hua Rao;Zhu-Jin Zhang;Yue-Dong Yang-.Imputing DNA Methylation by Transferred Learning Based Neural Network)[J].计算机科学技术学报（英文版）,2022(02):320-329
A类：
Imputing,Transferred,TDimpute,DNAmeth,imputing
B类：
Methylation,by,Learning,Based,Neural,Network,methylation,one,important,epigenetic,type,play,vital,role,many,diseases,including,cancers,With,development,high,throughput,sequencing,technology,there,much,progress,disclose,However,analyses,are,challenging,due,missing,values,caused,limitations,current,techniques,While,methods,have,been,developed,these,mostly,correlations,between,individual,samples,thus,limited,abnormal,In,this,study,present,novel,transfer,learning,neural,network,namely,learns,from,pan,then,fine,tunes,learned,over,each,specific,Tested,datasets,our,was,shown,outperform,other,commonly,Further,indicated,that,related,survival,biomarker,prognosis
AB值：
0.517825
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