title: Mapper: an intelligent restriction mapping tool.
abstract: BACKGROUND
  To determine the most powerful artificial intelligence techniques for automated restriction mapping, and use them to create a powerful multiple-enzyme restriction mapping tool.
  
  
  RESULTS
  The most effective search engine utilized model-driven exhaustive search and a form of binary logic pruning based on Pratt's separation theory. Additional experimentation led to the development of an input preprocessing module which significantly speeds up searches, and an output post-processing module which enables users to analyze large solution sets and reduce their apparent complexity.
  
  
  BACKGROUND
  An executable version of the resultant tool, Mapper, can be downloaded from our Web site (http://www.ai.eecs.uic.edu) by selecting the 'Software' option.
  
  
  BACKGROUND
  nelson@eecs.uic.edu (http://www.ai.eecs.uic.edu/ñelson).
authors.affiliation: Artificial Intelligence Laboratory, Department of Electrical Engineering and Computer Science, University of Illinois at Chicago, 60607-7053, USA.
authors.forename: J A
authors.initials: JA
authors.lastname: Inglehart
authors.forename: P C
authors.initials: PC
authors.lastname: Nelson
authors.forename: Y
authors.initials: Y
authors.lastname: Zou
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology; D005069:Evaluation Studies as Topic; D015183:Restriction Mapping / Q000379:methods* / Q000706:statistics & numerical data; D012984:Software