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. 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. 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 biological data sets. The effectiveness of this algorithm is demonstrated on various biological and medical data sets.
Raymer, M. L.,
Kuhn, L. A.,
& Punch, W. F.
(2001). Knowledge Discovery in Biological Datasets Using a Hybrid Bayes Classifier/Evolutionary Algorithm. Proceedings of the IEEE 2nd International Symposium on Bioinformatics and Bioengineering, 236-245.