Prediction of miRNA in Human MHC that Encodes Different Immunological Functions Using Support Vector Machines

ISSN: 2212-392X (Online)
ISSN: 1574-8936 (Print)


Volume 9, 5 Issues, 2014


Download PDF Flyer




Current Bioinformatics

Aims & ScopeAbstracted/Indexed in

Ranking and Category:
  • 20th of 52 in Mathematical & Computational Biology

Submit Abstracts Online Submit Manuscripts Online

Editor-in-Chief:
Alessandro Giuliani
Istituto Superiore di Sanitá (Italian NIH) Environment and Health Dept
Roma
Italy


View Full Editorial Board

Subscribe Purchase Articles Order Reprints

Current: 1.726
5 - Year: 1.577

Prediction of miRNA in Human MHC that Encodes Different Immunological Functions Using Support Vector Machines

Author(s): Archana Prabahar and Jeyakumar Natarajan

Affiliation: Data mining and Text mining Laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore- 641046, India.

Abstract

MicroRNAs (miRNAs) are short non-coding RNAs known to be involved in the gene regulatory functions in human. Major histocompatibility complex (MHC) located on the short arm of chromosome 6 remains as one of the most important regions associated with several human diseases. The complex spans ~4 Mb and covers >120 expressed genes. Gene expression at transcriptional and post transcriptional level is modulated by microRNA (miRNA) in collision with sequence polymorphism and epigenetic factors. In this study, we aim to predict miRNA responsible for different immunological functions and disorders in MHC region. Sequential and structural features of microRNAs were used for the classification of miRNA and other non-coding RNA data. Support vector machine (SVM) classifier was used for prediction and evaluated by jackknife validation technique. Overall accuracy was found to be 97.56% using leave-one-out cross validation technique. These experimental results confirm that our classification method predicts immune related miRNA with high accuracy.

Keywords: microRNA, leave-one-out cross validation, major histocompatability complex, support vector machine.

Purchase Online Order Reprints Order Eprints Rights and Permissions

  
  



Article Details

Volume: 9
Issue Number: 3
First Page: 343
Last Page: 347
Page Count: 5
DOI: 10.2174/1574893608666131120002036
Advertisement

Related Journals




Webmaster Contact: urooj@benthamscience.org Copyright © 2014 Bentham Science