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Computational methods for the identification of mature microRNAs within their Pre-miRNA

  • Ying Wang
  • , Xuefeng Dai
  • , Jidong Ru
  • , Dan Lv
  • , Jin Li
  • Qiqihar University
  • Harbin Engineering University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The urgent demand in miRNA research has call for the high performance computational methods for mature miRNA identification to supplement the biological experiment methods. In this study, we analyzed the secondary structure of pre-miRNA and extracted the important features. Then the current computational methods are investigated, and the flow chart of mature miRNAs location prediction methods is summarized. In addition, the current methods and algorithms are classified and assessed. Notably, we compare five machine learning algorithms of Naive Bayes, SVM, Random Forest, the Conditional Random Field and Adaboosting for mature miRNA-located prediction. Empirical findings indicated that SVM algorithm could achieve better performance than Naive Bayes method. And the Random Forest method is comparable to the performance of SVM, it shows good performance in this subject.

Original languageEnglish
Title of host publicationProceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015
EditorsLipo Wang, Sen Lin, Zhiyong Tao, Bing Zeng, Xiaowei Hui, Liangshan Shao, Jie Liang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1241-1245
Number of pages5
ISBN (Electronic)9781467390989
DOIs
StatePublished - 16 Feb 2016
Externally publishedYes
Event8th International Congress on Image and Signal Processing, CISP 2015 - Shenyang, China
Duration: 14 Oct 201516 Oct 2015

Publication series

NameProceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015

Conference

Conference8th International Congress on Image and Signal Processing, CISP 2015
Country/TerritoryChina
CityShenyang
Period14/10/1516/10/15

Keywords

  • algorithm
  • computational methods
  • machine learning
  • microRNA
  • pre-miRNA

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