Knowledge Discovery in Medical and Biological Datasets using a Hybrid Bayes Classifier/Evolutionary Algorithm

Michael L. Raymer, Travis E. Doom, Leslie A. Kuhn, William F. Punch

Research output: Contribution to journalArticlepeer-review

Abstract

A key element of bioinformatics research is the extraction of meaningful information from large experimental data sets. Various approaches, including statistical and graph theoretical methods, data mining, and computational pattern recognition, have been applied to this task with varying degrees of success. Using a novel classifier based on the Bayes discriminant function, we present a hybrid algorithm that employs feature selection and extraction to isolate salient features from large medical and other biological data sets. We have previously shown that a genetic algorithm coupled with a k-nearest-neighbors classifier performs well in extracting information about protein-water binding from X-ray crystallographic protein structure data. The effectiveness of the hybrid EC-Bayes classifier is demonstrated to distinguish the features of this data set that are the most statistically relevant and to weight these features appropriately to aid in the prediction of solvation sites.

Original languageAmerican English
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume33
DOIs
StatePublished - Oct 1 2003

Keywords

  • Bioinformatics
  • Evolutionary Computing
  • Genetic Algorithms
  • Pattern Recognition

Disciplines

  • Bioinformatics
  • Communication
  • Communication Technology and New Media
  • Computer Sciences
  • Databases and Information Systems
  • Life Sciences
  • OS and Networks
  • Physical Sciences and Mathematics
  • Science and Technology Studies
  • Social and Behavioral Sciences

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