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Plant MicroRNA Prediction by Supervised Machine Learning Using C5.0 Decision Trees
Joint Authors
Williams, Philip H.
Weiller, Georg
Eyles, Rod
Source
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-11-07
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
MicroRNAs (miRNAs) are nonprotein coding RNAs between 20 and 22 nucleotides long that attenuate protein production.
Different types of sequence data are being investigated for novel miRNAs, including genomic and transcriptomic sequences.
A variety of machine learning methods have successfully predicted miRNA precursors, mature miRNAs, and other nonprotein coding sequences.
MirTools, mirDeep2, and miRanalyzer require “read count” to be included with the input sequences, which restricts their use to deep-sequencing data.
Our aim was to train a predictor using a cross-section of different species to accurately predict miRNAs outside the training set.
We wanted a system that did not require read-count for prediction and could therefore be applied to short sequences extracted from genomic, EST, or RNA-seq sources.
A miRNA-predictive decision-tree model has been developed by supervised machine learning.
It only requires that the corresponding genome or transcriptome is available within a sequence window that includes the precursor candidate so that the required sequence features can be collected.
Some of the most critical features for training the predictor are the miRNA:miRNA∗ duplex energy and the number of mismatches in the duplex.
We present a cross-species plant miRNA predictor with 84.08% sensitivity and 98.53% specificity based on rigorous testing by leave-one-out validation.
American Psychological Association (APA)
Williams, Philip H.& Eyles, Rod& Weiller, Georg. 2012. Plant MicroRNA Prediction by Supervised Machine Learning Using C5.0 Decision Trees. Journal of Nucleic Acids،Vol. 2012, no. 2012, pp.1-10.
https://search.emarefa.net/detail/BIM-488494
Modern Language Association (MLA)
Williams, Philip H.…[et al.]. Plant MicroRNA Prediction by Supervised Machine Learning Using C5.0 Decision Trees. Journal of Nucleic Acids No. 2012 (2012), pp.1-10.
https://search.emarefa.net/detail/BIM-488494
American Medical Association (AMA)
Williams, Philip H.& Eyles, Rod& Weiller, Georg. Plant MicroRNA Prediction by Supervised Machine Learning Using C5.0 Decision Trees. Journal of Nucleic Acids. 2012. Vol. 2012, no. 2012, pp.1-10.
https://search.emarefa.net/detail/BIM-488494
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-488494