Regulatory motif discovery using a population clustering evolutionary algorithm.

@article{citeulike:1805301, abstract = {This paper describes a novel evolutionary algorithm for regulatory motif discovery in DNA promoter sequences. The algorithm uses data clustering to logically distribute the evolving population across the search space. Mating then takes place within local regions of the population, promoting overall solution diversity and encouraging discovery of multiple solutions. Experiments using synthetic data sets have demonstrated the algorithm's capacity to find position frequency matrix models of known regulatory motifs in relatively long promoter sequences. These experiments have also shown the algorithm's ability to maintain diversity during search and discover multiple motifs within a single population. The utility of the algorithm for discovering motifs in real biological data is demonstrated by its ability to find meaningful motifs within muscle-specific regulatory sequences.}, address = {Intelligent Systems Research Group, Department of Electronics, University of York, York, UK. mal503@ohm.york.ac.uk}, author = {Lones, M. A. and Tyrrell, A. M. }, citeulike-article-id = {1805301}, doi = {http://dx.doi.org/10.1109/tcbb.2007.1044}, issn = {1545-5963}, journal = {IEEE/ACM Trans Comput Biol Bioinform}, keywords = {motif-discovery, tool}, number = {3}, pages = {403--414}, posted-at = {2007-10-22 09:26:24}, priority = {4}, title = {Regulatory motif discovery using a population clustering evolutionary algorithm.}, url = {http://dx.doi.org/10.1109/tcbb.2007.1044}, volume = {4}, year = {2007} }

TY - JOUR ID - citeulike:1805301 L3 - citeulike-article-id:1805301 TI - Regulatory motif discovery using a population clustering evolutionary algorithm. JF - IEEE/ACM Trans Comput Biol Bioinform VL - 4 IS - 3 SP - 403 EP - 414 AD - Intelligent Systems Research Group, Department of Electronics, University of York, York, UK. mal503@ohm.york.ac.uk SN - 1545-5963 N2 - This paper describes a novel evolutionary algorithm for regulatory motif discovery in DNA promoter sequences. The algorithm uses data clustering to logically distribute the evolving population across the search space. Mating then takes place within local regions of the population, promoting overall solution diversity and encouraging discovery of multiple solutions. Experiments using synthetic data sets have demonstrated the algorithm's capacity to find position frequency matrix models of known regulatory motifs in relatively long promoter sequences. These experiments have also shown the algorithm's ability to maintain diversity during search and discover multiple motifs within a single population. The utility of the algorithm for discovering motifs in real biological data is demonstrated by its ability to find meaningful motifs within muscle-specific regulatory sequences. KW - motif-discovery KW - tool AU - Lones, MA AU - Tyrrell, AM PY - 2007///p UR - http://dx.doi.org/10.1109/tcbb.2007.1044 ER -