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JACIII Vol.18 No.4 pp. 474-479
doi: 10.20965/jaciii.2014.p0474
(2014)

Paper:

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Protein Structural Class Prediction via k-Separated Bigrams Using Position Specific Scoring Matrix

Harsh Saini*, Gaurav Raicar*, Alok Sharma*,**,
Sunil Lal*, Abdollah Dehzangi**, Rajeshkannan Ananthanarayanan*,
James Lyons**, Neela Biswas***, and Kuldip K. Paliwal**

*The University of the South Pacific, Fiji, Laucala Bay, Suva, Fiji

**Griffith University, Brisbane, Australia

***Royal Brisbane and Women’s Hospital, Brisbane, Australia

Received:
December 20, 2013
Accepted:
February 15, 2014
Published:
July 20, 2014
Creative Commons License
Keywords:
SCOP, structural class prediction, PSSM, bigram, k-separated bigram, transition probabilities, SVM
Abstract
Protein structural class prediction (SCP) is as important task in identifying protein tertiary structure and protein functions. In this study, we propose a feature extraction technique to predict secondary structures. The technique utilizes bigram (of adjacent and k-separated amino acids) information derived from Position Specific Scoring Matrix (PSSM). The technique has shown promising results when evaluated on benchmarked Ding and Dubchak dataset.
Cite this article as:
H. Saini, G. Raicar, A. Sharma, S. Lal, A. Dehzangi, R. Ananthanarayanan, J. Lyons, N. Biswas, and K. Paliwal, “Protein Structural Class Prediction via k-Separated Bigrams Using Position Specific Scoring Matrix,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.4, pp. 474-479, 2014.
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