alexa N-W Algorithm and ANFIS Modeling on Alignment Similarity of Triplex Capsid Protein of Human Herpes Simplex Virus | OMICS International | Abstract
ISSN: 2153-0602

Journal of Data Mining in Genomics & Proteomics
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Research Article

N-W Algorithm and ANFIS Modeling on Alignment Similarity of Triplex Capsid Protein of Human Herpes Simplex Virus

Vipan Kumar Sohpal1*, Apurba Dey2 and Amarpal Singh3

1Department of Chemical and Bio Technology, Beant College of Engineering and Tech, Gurdaspur, Punjab, India

2Department of Bio Technology, National Institute of Technology, Durgapur, West Bengal, India

3Department of Electronics and Communication Engg, Beant College of Engineering and Tech, Gurdaspur, Punjab, India

*Corresponding Author:
Vipan Kumar Sohpal
Department of Chemical and Bio Technology
Beant College of Engineering and Tech
Gurdaspur, Punjab, India
E-mail: [email protected]

Received Date: March 04, 2013; Accepted Date: March 27, 2013; Published Date: April 04, 2013

Citation: Vipan Kumar S, Dey A, Singh A (2013) N-W Algorithm and ANFIS Modeling on Alignment Similarity of Triplex Capsid Protein of Human Herpes Simplex Virus. J Data Mining Genomics Proteomics S3:001. doi:10.4172/2153-0602.S3-001

Copyright: © 2013 Vipan Kumar S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Optimal sequence similarity of triplex capsid proteins of human herpes simplex virus (HHV) is a complex bioinformatics problem, which is controlled by alignment algorithms, substitution matrix, gap penalty and gap extension. A precise choice of mutation matrix is required to optimal the alignment similarity and appropriate computational approach required for similarity search. The present paper uses Adaptive Neuro-Fuzzy Inference System (ANFIS) approach to model and simulate the alignment similarity for PAM and Blosum substitution matrices. Mutation matrix and sequences of HHV-I and HHV-II were taken as model’s input parameters. The model is the combination of fuzzy inference, artificial neural network, and set of fuzzy rules has been developed directly from computational analysis using N-W algorithm. The proposed modeling approach is verified by comparing the expected results with the observed practical results obtained by computational analysis under specific conditions. The application of ANFIS test shows that the substitution matrix predicted by a proposed model is fully in agreement with the experimental values at 0.5% level of significance.

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