Optimizing genetic algorithm for motif discovery

  • Authors:
  • Hongwei Huo;Zhenhua Zhao;Vojislav Stojkovic;Lifang Liu

  • Affiliations:
  • School of Computer Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China;School of Computer Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China;Computer Science Department, Morgan State University, Baltimore, MD 21251, USA;School of Computer Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China

  • Venue:
  • Mathematical and Computer Modelling: An International Journal
  • Year:
  • 2010

Quantified Score

Hi-index 0.98

Visualization

Abstract

Planted (l,d)-motif identification is an important and challenging problem in computational biology. In this paper, we present an original algorithm (GARPS) that combines Genetic Algorithm (GA) and Random Projection Strategy (RPS) to identify (l,d)-motifs. We start with RPS to find good starting positions by introducing position-weighted function and hash each of all l-mers in the input sequences onto the corresponding k-dimensional (k