A Bayesian Scoring Scheme based Particle Swarm Optimization algorithm to identify transcription factor binding sites

Karabulut M., İBRİKÇİ T.

APPLIED SOFT COMPUTING, cilt.12, sa.9, ss.2846-2855, 2012 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 12 Sayı: 9
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1016/j.asoc.2012.04.006
  • Dergi Adı: APPLIED SOFT COMPUTING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2846-2855
  • Çukurova Üniversitesi Adresli: Evet

Özet

Identification of transcription factor binding sites is a vital task in contemporary biology, since it helps researchers to comprehend the regulatory mechanism of gene expression. Computational tools to perform this task have gained great attention since they are good alternatives to expensive and laborious biological experiments. In this paper, we propose a Particle Swarm Optimization based motif-finding method that utilizes a proven Bayesian Scoring Scheme as the fitness function. Since PSO is designed to work in multidimensional continuous domains, this paper presents required developments to adapt PSO for the motif finding application. Furthermore, this paper presents a benchmark of PSO variants with four separate population topologies, GBest, Bidirectional Ring, Random and Von Neumann. Simulations performed over synthetic and real data sets have shown that the proposed method is efficient and also superior to some well-known existing tools. Additionally, the Bidirectional Ring topology appears to be remarkable for the motif-finding application. (C) 2012 Elsevier B.V. All rights reserved.

Identification of transcription factor binding sites is a vital task in contemporary biology, since it helps researchers to comprehend the regulatory mechanism of gene expression. Computational tools to perform this task have gained great attention since they are good alternatives to expensive and laborious biological experiments. In this paper, we propose a Particle Swarm Optimization based motif-finding method that utilizes a proven Bayesian Scoring Scheme as the fitness function. Since PSO is designed to work in multidimensional continuous domains, this paper presents required developments to adapt PSO for the motif finding application. Furthermore, this paper presents a benchmark of PSO variants with four separate population topologies, GBest, Bidirectional Ring, Random and Von Neumann. Simulations performed over synthetic and real data sets have shown that the proposed method is efficient and also superior to some well-known existing tools. Additionally, the Bidirectional Ring topology appears to be remarkable for the motif-finding application.