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InfoEvolve: moving from data to knowledge using information theory and genetic algorithms.

Ganesh Vaidyanathan
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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.

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AlgorithmsComputational BiologyDNAModels, GeneticModels, TheoreticalReproducibility of ResultsJournal Article

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Ganesh Vaidyanathan. 2004. InfoEvolve: moving from data to knowledge using information theory and genetic algorithms.. https://doi.org/10.1196/annals.1310.019

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