title: Stochastic heuristic algorithms for target motif identification (extended abstract).
abstract: Target motifs are motifs that are "close" to one or more substrings in each sequence in one given set of sequences but are far from every substring in another given set of sequences. Target motifs have pharmaceutical applications; unfortunately, the problem of identifying target motifs is NP-hard and is thus unlikely to have efficient optimal solution algorithms. In this paper, we propose a set of simple modifications to the Gibbs Sampling heuristic for finding motifs which allows this heuristic to detect target motifs. We also present the results of several experiments relative to both simulated and real datasets which suggest that this modified heuristic is good at detecting target motifs under a variety of conditions.
authors.affiliation: Department of Computing and Software, McMaster University, Hamilton, ON, Canada.
authors.forename: H T
authors.initials: HT
authors.lastname: Wareham
authors.forename: T
authors.initials: T
authors.lastname: Jiang
authors.forename: X
authors.initials: X
authors.lastname: Zhang
authors.forename: C G
authors.initials: CG
authors.lastname: Trendall
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D016208:Databases, Factual; D013269:Stochastic Processes; D009626:Terminology as Topic