title: Combinatorial motif analysis and hypothesis generation on a genomic scale.
abstract: BACKGROUND
  Computer-assisted methods are essential for the analysis of biosequences. Gene activity is regulated in part by the binding of regulatory molecules (transcription factors) to combinations of short motifs. The goal of our analysis is the development of algorithms to identify regulatory motifs and to predict the activity of combinations of those motifs.
  
  
  METHODS
  Our research begins with a new motif-finding method, using multiple objective functions and an improved stochastic iterative sampling strategy. Combinatorial motif analysis is accomplished by constructive induction that analyzes potential motif combinations. The hypothesis is generated by applying standard inductive learning algorithms.
  
  
  RESULTS
  Tests using 10 previously identified regulons from budding yeast and 14 artificial families of sequences demonstrated the effectiveness of the new motif-finding method. Motif combination and classification approaches were used in the analysis of a sample DNA array data set derived from genome-wide gene expression analysis.
  
  
  BACKGROUND
  Programs will be available as executable files upon request.
  
  
  BACKGROUND
  yhu@ics.uci.eduor yhu@cse.ttu.edu.tw
authors.affiliation: Information and Computer Science Department, Department of Biological Chemistry, College of Medicine, University of California, Irvine, USA.
authors.forename: Y J
authors.initials: YJ
authors.lastname: Hu
authors.forename: S
authors.initials: S
authors.lastname: Sandmeyer
authors.forename: C
authors.initials: C
authors.lastname: McLaughlin
authors.forename: D
authors.initials: D
authors.lastname: Kibler
mesh_terms: D000465:Algorithms; D005800:Genes, Fungal; D012045:Regulatory Sequences, Nucleic Acid; D018085:Regulon; D012441:Saccharomyces cerevisiae / Q000235:genetics