Prediction of mammalian microRNA binding sites using random forests

Ching Yi Chen*, Chun Hung Su, I. Fang Chung, Nikhil R. Pal

*Corresponding author for this work

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

3 Scopus citations

Abstract

In biological systems, microRNAs involve in the regulation of their target genes by degrading the targeted binding mRNAs or by repressing the corresponding protein products. MicroRNAs are shown to play important roles in numerous biological processes, such as disease formation, especially in cancer. Predicting the target binding site of microRNA can help to identify the novel miroRNA target genes. Either microRNAs or its target genes can be recognized as biomarkers for diagnosis of diseases, prediction of prognosis, or even therapy decision. In this study, first we apply support vector machines (SVMs), neural networks and decision tree-based approaches to select a set of useful features, which represent important characteristics for the determination of the interaction between microRNA and its target binding mRNA sequence. Next, these selected features are used in two classifiers, SVM and Random Forests, to perform prediction of microRNA target sites. The features that are selected by Random Forests itself exhibit the best performance for predicting the binding site of microRNA. Its prediction accuracy can reach about 75%.

Original languageEnglish
Title of host publicationProceedings 2012 International Conference on System Science and Engineering, ICSSE 2012
Pages91-95
Number of pages5
DOIs
StatePublished - 2012
Event2012 International Conference on System Science and Engineering, ICSSE 2012 - Dalian, Liaoning, China
Duration: 30 Jun 20122 Jul 2012

Publication series

NameProceedings 2012 International Conference on System Science and Engineering, ICSSE 2012

Conference

Conference2012 International Conference on System Science and Engineering, ICSSE 2012
Country/TerritoryChina
CityDalian, Liaoning
Period30/06/122/07/12

Keywords

  • binding site
  • feature selection
  • machine learning
  • microRNA
  • Random Forests
  • target site

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