Identification of plant precursor mirnas using structural robustness and secondary structures features

Conference proceedings article

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Publication Details

Author list: Anuntakarun S., Wattanapornprom W., Lertampaiporn S.

Publisher: Hindawi

Publication year: 2017

Volume number: Part F131935

Start page: 1

End page: 4

Number of pages: 4

ISBN: 9781450352970

ISSN: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85038382131&doi=10.1145%2f3143344.3143347&partnerID=40&md5=860225caa045b597e16b2bb22477174e

Languages: English-Great Britain (EN-GB)

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Abstract

This work presents an identification tool for plant precursor miRNAs (pre-miRNAs) using structural robustness and derivative features which can improve performance in discriminating the plant pre-miRNAs from pseudo pre-miRNAs. The classification models were trained with plant pre-miRNAs and pseudo hairpins datasets from PlantMiRNAPred web site. The top 20 features were selected from four groups of features including sequence-based features, secondary structure features, base-pair features and a self-containment index score. In particular, the self-containment index score was found to be the highest informative feature among the 20 selected features. Ten-fold cross validation was applied to choose a classifier algorithm with the highest performance being among Support Vector Machine, Random Forest, Decision Tree, Na๏ve Bayes, K-nearest neighbor, Back-propagation Neural Network, Ripper and RBF network based on the ROC area and accuracy. The results demonstrated that the Random Forest model using 20 selected features achieved 97% accuracy and 94% sensitivity in test sets to discriminating real plant pre-miRNAs from others. ฉ 2017 Association for Computing Machinery.

Keywords

Precursor miRNAs, Self-containment index score