Please use this identifier to cite or link to this item:
Title: Efficient and Effective Multiple Protein Sequence Alignment Model Using Dynamic Progressive Approach with Novel Look Back Ahead Scoring System
Authors: Bankapur, S.
Patil, N.
Issue Date: 2017
Citation: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, Vol.10597 LNCS, , pp.397-404
Abstract: Multiple protein sequence alignment is the elementary hurdle towards addressing further challenges like prediction of protein structure and its functions, protein sub-cellular localization, drug discovery etc. For the last 3 decades numerous models have been proposed to address this challenge however the models are either computationally complex or not effective with respect to aligned results. In this paper, a computationally efficient and effective model is proposed to solve multiple protein sequence alignment. Our proposed model follows dynamic progressive global alignment approach in which a sequence pair is merged dynamically based on novel scoring system, named Look Back Ahead (LBA). Proposed model results were validated with aligned reference results on benchmark datasets (PREFAB4refm and SABrem), using four metrics: Sum-of-Pairs (SP), Total Gap Penalty (TGP), Column Score (CS) and Total Mutation Count Pair-wise (TMCP). Experimental results demonstrate that the proposed method outperforms benchmark reference results in any three evaluation metrics by 77.46% and 68.65% for PREFAB4refm and SABrem datasets respectively. � 2017, Springer International Publishing AG.
Appears in Collections:2. Conference Papers

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.