An expert system to classify microarray gene expression data using gene selection by decision tree

Authors:
Jorng-Tzong Horng;Li-Cheng Wu;Baw-Juine Liu;Jun-Li Kuo;Wen-Horng Kuo;Jin-Jian Zhang
Affiliations:
Department of Computer Science and Information Engineering, National Central University, Chung-Li, Taiwan and Institute of System Biology and Bioinformatics, National Central University, Taiwan an ...;Institute of System Biology and Bioinformatics, National Central University, Taiwan;Department of Computer Science and Information Engineering, Yuan Ze University, Taiwan;Department of Computer Science and Information Engineering, National Central University, Chung-Li, Taiwan;College of Medicine, National Taiwan University, Taiwan;College of Medicine, National Taiwan University, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 14
Cited 7

Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Optimization models for cancer classification: extracting gene interaction information from microarray expression data

Bioinformatics
A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression

Bioinformatics
Gene selection using a two-level hierarchical Bayesian model

Bioinformatics
HykGene: a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data

Bioinformatics
A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis

Bioinformatics
Outcome signature genes in breast cancer: is there a unique set?

Bioinformatics
Bayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data

Bioinformatics
Ensemble dependence model for classification and prediction of cancer and normal gene expression data

Bioinformatics
Biomarker discovery in microarray gene expression data with Gaussian processes

Bioinformatics
Differential coexpression analysis using microarray data and its application to human cancer

Bioinformatics
PCP: a program for supervised classification of gene expression profiles

Bioinformatics
Exploiting scale-free information from expression data for cancer classification

Computational Biology and Chemistry
Estimating continuous distributions in Bayesian classifiers

UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence

Partition-conditional ICA for Bayesian classification of microarray data

Expert Systems with Applications: An International Journal
Robust approach for estimating probabilities in Naïve-Bayes Classifier for gene expression data

Expert Systems with Applications: An International Journal
Bi-level weights sum method for shock diagnosis

Expert Systems with Applications: An International Journal
An efficient statistical feature selection approach for classification of gene expression data

Journal of Biomedical Informatics
Design of fuzzy expert system for microarray data classification using a novel Genetic Swarm Algorithm

Expert Systems with Applications: An International Journal
genEnsemble: A new model for the combination of classifiers and integration of biological knowledge applied to genomic data

Expert Systems with Applications: An International Journal
Review article: Computational intelligence techniques in bioinformatics

Computational Biology and Chemistry

Quantified Score

Hi-index	12.06

Visualization

Abstract

Gene selection can help the analysis of microarray gene expression data. However, it is very difficult to obtain a satisfactory classification result by machine learning techniques because of both the curse-of-dimensionality problem and the over-fitting problem. That is, the dimensions of the features are too large but the samples are too few. In this study, we designed an approach that attempts to avoid these two problems and then used it to select a small set of significant biomarker genes for diagnosis. Finally, we attempted to use these markers for the classification of cancer. This approach was tested the approach on a number of microarray datasets in order to demonstrate that it performs well and is both useful and reliable.