Knowledge Discovery in Biological Datasets Using a Hybrid Bayes Classifier/Evolutionary Algorithm

Michael L. Raymer; Leslie A. Kuhn; William F. Punch

doi:10.1109/BIBE.2001.974435

Knowledge Discovery in Biological Datasets Using a Hybrid Bayes Classifier/Evolutionary Algorithm

Michael L. Raymer, Leslie A. Kuhn, William F. Punch

Research output: Contribution to conference › Poster

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.

Original language	American English
DOIs	https://doi.org/10.1109/BIBE.2001.974435
State	Published - Nov 1 2001
Event	Proceedings of the 2nd Annual IEEE International Symposium on Bioinformatics Bioengineering - Duration: Nov 1 2001 → …

Conference

Conference	Proceedings of the 2nd Annual IEEE International Symposium on Bioinformatics Bioengineering
Period	11/1/01 → …

Keywords

Feature Extraction
Feature Selection
Genetic Algorithms
Pattern Classification
Bayes Classifier
Curse of Dimensionality
Protein Solvation

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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Knowledge Discovery in Biological Datasets Using a Hybrid Bayes CFinal published version, 274 KBLicense: CC BY

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title = "Knowledge Discovery in Biological Datasets Using a Hybrid Bayes Classifier/Evolutionary Algorithm",

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.",

keywords = "Feature Extraction, Feature Selection, Genetic Algorithms, Pattern Classification, Bayes Classifier, Curse of Dimensionality, Protein Solvation",

author = "Raymer, {Michael L.} and Kuhn, {Leslie A.} and Punch, {William F.}",

year = "2001",

month = nov,

day = "1",

doi = "10.1109/BIBE.2001.974435",

language = "American English",

note = "Proceedings of the 2nd Annual IEEE International Symposium on Bioinformatics Bioengineering ; Conference date: 01-11-2001",

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T1 - Knowledge Discovery in Biological Datasets Using a Hybrid Bayes Classifier/Evolutionary Algorithm

AU - Raymer, Michael L.

AU - Kuhn, Leslie A.

AU - Punch, William F.

PY - 2001/11/1

Y1 - 2001/11/1

N2 - 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.

AB - 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.

KW - Feature Extraction

KW - Feature Selection

KW - Genetic Algorithms

KW - Pattern Classification

KW - Bayes Classifier

KW - Curse of Dimensionality

KW - Protein Solvation

UR - https://corescholar.libraries.wright.edu/knoesis/919

U2 - 10.1109/BIBE.2001.974435

DO - 10.1109/BIBE.2001.974435

M3 - Poster

T2 - Proceedings of the 2nd Annual IEEE International Symposium on Bioinformatics Bioengineering

Y2 - 1 November 2001

ER -

Knowledge Discovery in Biological Datasets Using a Hybrid Bayes Classifier/Evolutionary Algorithm

Abstract

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Keywords

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