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Reseach Article

Planted (l, d) - Motif Finding using Particle Swarm Optimization

Published on None 2010 by U.Srinivasulu Reddy, Michael Arock, A.V.Reddy
Evolutionary Computation for Optimization Techniques
Foundation of Computer Science USA
ECOT - Number 2
None 2010
Authors: U.Srinivasulu Reddy, Michael Arock, A.V.Reddy

U.Srinivasulu Reddy, Michael Arock, A.V.Reddy . Planted (l, d) - Motif Finding using Particle Swarm Optimization. Evolutionary Computation for Optimization Techniques. ECOT, 2 (None 2010), 51-56.

author = { U.Srinivasulu Reddy, Michael Arock, A.V.Reddy },
title = { Planted (l, d) - Motif Finding using Particle Swarm Optimization },
journal = { Evolutionary Computation for Optimization Techniques },
issue_date = { None 2010 },
volume = { ECOT },
number = { 2 },
month = { None },
year = { 2010 },
issn = 0975-8887,
pages = { 51-56 },
numpages = 6,
url = { /specialissues/ecot/number2/1541-144/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Special Issue Article
%1 Evolutionary Computation for Optimization Techniques
%A U.Srinivasulu Reddy
%A Michael Arock
%A A.V.Reddy
%T Planted (l, d) - Motif Finding using Particle Swarm Optimization
%J Evolutionary Computation for Optimization Techniques
%@ 0975-8887
%N 2
%P 51-56
%D 2010
%I International Journal of Computer Applications

In Bioinformatics, Motif Finding is one of the most popular problems, which has many applications. Generally, it is to locate recurring patterns in the sequence of nucleotides or amino acids. As we can’t expect the pattern to be exact matching copies owing to biological mutations, the motif finding turns to be an NP-complete problem. By approximating the same in different aspects, scientists have provided many solutions in the literature. The most of the algorithms suffer with local optima. Particle swarm optimization (PSO) is a new global optimization technique which has wide applications. It finds the global best solution by simply adjusting the trajectory of each individual towards its own best location and towards the best particle of the swarm at each generation. We have adopted the features of the PSO to solve the Planted Motif Finding Problem and have designed a sequential algorithm. We have performed experiments with simulated data it outperforms MbGA and PbGA. The PMbPSO also applied for real biological data sets and observe that the algorithm is also able to detect known TFBS accurately when there are no mutations.

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Index Terms

Computer Science
Information Sciences


Motif Finding Particle Swarm Optimization (PSO) Swarm Intelligence (SI) Transcriptional Factor Binding Sites (TFBS) Planted Motifs