Document Type
Conference Proceeding
Publication Date
11-2001
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. 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.
Repository Citation
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.
https://corescholar.libraries.wright.edu/knoesis/936
DOI
10.1109/BIBE.2001.974435
Included in
Bioinformatics Commons, Communication Technology and New Media Commons, Databases and Information Systems Commons, OS and Networks Commons, Science and Technology Studies Commons
Comments
Presented at the 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering, Bethesda, MD, November 4-6, 2001.
Posted with permission from IEEE.