Identification and prediction of key nucleotide sites using machine learning in bioinformatics: A brief overview - Details

author：

Cai, Jianhua (Cai, Jianhua.) ^[1] | Wei, Leyi (Wei, Leyi.) ^[2] | Zeng, Kun (Zeng, Kun.) ^[3] | Xiao, Guobao (Xiao, Guobao.) ^[4]

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Abstract：

Nowadays,　the　burgeon　of　machine　learning　has　promoted　its　wide　application　in　various　fields.　In　Bioinformatics,　machine　learning　computational　method　has　become　an　indispensable　part.　Its　efficiency　and　simplicity　break　the　expensive　and　inefficient　barriers　of　previous　experimental　methods.　This　article　briefly　reviews　the　application　and　development　of　machine　learning　in　identifying　and　predicting　key　nucleotide　sites　(such　as　6mA,　5mC,　4mC,　etc.)　in　DNA　or　RNA.　Among　them,　supervised　learning　is　one　of　the　main　methods　to　this　work.　Its　wide　application　has　provided　great　convenience　for　the　development　of　identification　and　prediction　of　key　nucleotides　in　Bioinformatics.　©　2019　IEEE.

Keyword：

Big data Bioinformatics Cloud computing Forecasting Machine learning Nucleotides Predictive analytics Social networking (online)

Community：

[ 1 ] [Cai, Jianhua]Fujian Provincial Key Laboratory, Information Processing and Intelligent Control, Minjiang University, Fuzhou, China
[ 2 ] [Cai, Jianhua]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China
[ 3 ] [Wei, Leyi]College of Intelligence and Computing, Tianjin University, Tianjin, China
[ 4 ] [Zeng, Kun]Fujian Provincial Key Laboratory, Information Processing and Intelligent Control, Minjiang University, Fuzhou, China
[ 5 ] [Xiao, Guobao]Fujian Provincial Key Laboratory, Information Processing and Intelligent Control, Minjiang University, Fuzhou, China

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Identification and prediction of key nucleotide sites using machine learning in bioinformatics: A brief overview
2019，SocialCom 2019

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Year： 2019

Page： 1194-1200

Language： English

Cited Count：

WoS CC Cited Count： 0

SCOPUS Cited Count： 1

ESI Highly Cited Papers on the List： 0 Unfold All

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Chinese Cited Count：

30 Days PV： 4

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机械工程及自动化学院本学院/部未明确归属的数据

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