PSOMF: An algorithm for pattern discovery using PSO
datasetposted on 21.11.2017 by Zare-Mirakabad, F., Ahrabian, H., Sadeghi, M., Mohammadzadeh, J., Hashemifar, S., Nowzari-Dalini, A., Goliaei, B.
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
The task of transcription factor binding sites discovery from the upstream region of gene, without any prior knowledge of what look likes, is very challenging. In this paper we propose an algorithm based on Particle Swarm Optimization (PSO) to identify motif instances in multiple biological sequences. The experimental results on yeast sac-choromyces Cerevisae transcription factor binding sites, demonstrate that the proposed method is working analogous to YMF, MEME and AlignACE algorithms. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.