©2020-2032 All Rights Reserved. Online Journal of Bioinformatics . You may not store these pages in any form except for your own personal use. All other usage or distribution is illegal under international copyright treaties. Permission to use any of these pages in any other way besides the before mentioned must be gained in writing from the publisher. This article is exclusively copyrighted in its entirety to OJB publications. This article may be copied once but may not be, reproduced or re-transmitted without the express permission of the editors. This journal satisfies the refereeing requirements (DEST) for the Higher Education Research Data Collection (Australia). Linking:To link to this page or any pages linking to this page you must link directly to this page only here rather than put up your own page.


Online Journal of Bioinformatics©

Volume 21(2): 97-105, 2020..

A support vector machine for classification of plant and animal miRNA.


KR Pardasani1, Bhasker Pant2, Kumud Pant2.


Department(s) of 1Mathematics, 2Bioinformatics, MANIT, Bhopal, India. 




Pardasani KR, Pant B, Pant K., Support vector machine for classification of plants and animal miRNA, Onl J Bioinform., 21(2): 97-105, 2020.. MicroRNAs (miRNAs) constitute a large family of non-coding RNAs that function to regulate gene expression. Wet lab experiments used to classify miRNA of plants and animals are expensive, labor intensive and time consuming. Thus there arises a need for computational approach for classification of plants and animal miRNA. These computational approaches are fast and economical compared with wet lab techniques. We developed a support vector machine (SVM) learning Sequential Minimal Optimization (or SMO) algorithm for classification of plant and animal miRNA. The number of mismatches with target mRNA, presence of clusters, number of target genes and size of fold back loop used in the SVM model gave us 97% accuracy suggesting that these four characteristics must be included in any classifier of plant and animal miRNA’s.


Key-words: Support vector, classification, miRNA.