The discovery of microRNAs (miRNAs), small non-coding RNAs that regulate gene expression at the post-transcriptional level, has led to a shift in our understanding of the complexity in gene regulatory networks. One key question in understanding the effect of an individual miRNA is to identify its potential targets in a local or genome-wide regulation process. We define here a novel algorithm for computational prediction of miRNA target genes using solely the sequence information for mature miRNA and potential target mRNAs. Our method considers the aligned miRNA-mRNA duplex as a new sequence and extracts compositional features from newly constructed sequence to be fed into a Naive Bayes Classifier. A rigorous analysis in a common benchmark set reveals that, in terms of prediction accuracy, the new algorithm outperforms most of the available methods, while being competitive with more complex and relatively inefficient methods.

MicroRNA target recognition from compositional features of aligned microRNA-mRNA duplexes

C. Beyan;
2010-01-01

Abstract

The discovery of microRNAs (miRNAs), small non-coding RNAs that regulate gene expression at the post-transcriptional level, has led to a shift in our understanding of the complexity in gene regulatory networks. One key question in understanding the effect of an individual miRNA is to identify its potential targets in a local or genome-wide regulation process. We define here a novel algorithm for computational prediction of miRNA target genes using solely the sequence information for mature miRNA and potential target mRNAs. Our method considers the aligned miRNA-mRNA duplex as a new sequence and extracts compositional features from newly constructed sequence to be fed into a Naive Bayes Classifier. A rigorous analysis in a common benchmark set reveals that, in terms of prediction accuracy, the new algorithm outperforms most of the available methods, while being competitive with more complex and relatively inefficient methods.
2010
microRNA, RNA, gene expression data, machine learning, Naive Bayes Classifier
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1121926
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