title: InfoEvolve: moving from data to knowledge using information theory and genetic algorithms.
abstract: InfoEvolve is a unified suite of data mining and empirical modeling tools capable of discovering low-bias and low-variance solutions to complex processes. The method is based on a common set of principles involving information theory and genetic algorithms. InfoEvolve can also discover multiple strategies embedded in complex data sets for achieving a desired target or goal. This latter aspect may prove to be very useful in drug design. The paper analyzes the following: InfoEvolve from a theoretical standpoint; a conceptual overview of InfoEvolve with a short description of the modeling method; the method using the example of homogeneous identification of DNA from an analysis of its melting curve behavior; and key learnings and additional applications of the technology for both drug design and genome analysis.
authors.affiliation: Corporate Center for Engineering Research, E. I. DuPont de Nemours & Co., P. O. Box 80357, Wilmington, DE 19880-0357, USA. gvaidyanathan@comcast.net
authors.forename: Ganesh
authors.initials: G
authors.lastname: Vaidyanathan
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000235:genetics*; D008957:Models, Genetic; D008962:Models, Theoretical; D015203:Reproducibility of Results
chemical_list: D004247:DNA