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

Rianon Zaman, Shahana Yasmin Chowdhury, Mahmood A Rashid, Alok Sharma, Abdollah Dehzangi, Swakkhar Shatabda

Journal: BioMed research international 2018;2017():4590609

PMID: 29270430

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.

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