Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2017 (2017), Article ID 4590609, 10 pages
https://doi.org/10.1155/2017/4590609
Research Article

HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features

1Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh
2School of Computing, Information and Mathematical Sciences, The University of the South Pacific, Suva, Fiji
3Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia
4School of Engineering and Physics, The University of the South Pacific, Suva, Fiji
5RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
6Department of Computer Science, Morgan State University, Baltimore, MD, USA

Correspondence should be addressed to Swakkhar Shatabda; db.ca.uiu.esc@rahkkaws

Received 29 August 2017; Accepted 22 October 2017; Published 14 November 2017

Academic Editor: Paul Harrison

Copyright © 2017 Rianon Zaman et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM) as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.