Gene boosting for cancer classification based on gene expression profiles

Authors:
Jin-Hyuk Hong;Sung-Bae Cho
Affiliations:
Department of Computer Science, Yonsei University, 134 Shinchon-dong, Sudaemoon-ku, Seoul 120-749, Republic of Korea;Department of Computer Science, Yonsei University, 134 Shinchon-dong, Sudaemoon-ku, Seoul 120-749, Republic of Korea
Venue:
Pattern Recognition
Year:
2009

Citing 15
Cited 2

Improved Boosting Algorithms Using Confidence-rated Predictions

Machine Learning - The Eleventh Annual Conference on computational Learning Theory
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
BagBoosting for tumor classification with gene expression data

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
A semiparametric approach for marker gene selection based on gene expression data

Bioinformatics
Multiclass cancer classification and biomarker discovery using GA-based algorithms

Bioinformatics
Analysis of recursive gene selection approaches from microarray data

Bioinformatics
Incremental wrapper-based gene selection from microarray data for cancer classification

Pattern Recognition
A Blocking Strategy to Improve Gene Selection for Classification of Gene Expression Data

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Markov blanket-embedded genetic algorithm for gene selection

Pattern Recognition
Development of Two-Stage SVM-RFE Gene Selection Strategy for Microarray Expression Data Analysis

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Evolutionary Rough Feature Selection in Gene Expression Data

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Filter versus wrapper gene selection approaches in DNA microarray domains

Artificial Intelligence in Medicine

Rough set based maximum relevance-maximum significance criterion and Gene selection from microarray data

International Journal of Approximate Reasoning
Boosting regression methods based on a geometric conversion approach: Using SVMs base learners

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

Gene selection is one of the important issues for cancer classification based on gene expression profiles. Filter and wrapper approaches are widely used for gene selection, where the former is hard to measure the relationship between genes and the latter requires lots of computation. We present a novel method, called gene boosting, to select relevant gene subsets by integrating filter and wrapper approaches. It repeatedly selects a set of top-ranked informative genes by a filtering algorithm with respect to a temporal training dataset constructed according to the classification result for the original training dataset. Empirical results on three microarray benchmark datasets have shown that the proposed method is effective and efficient in finding a relevant and concise gene subset. It achieved competitive performance with fewer genes in a reasonable time, as well as led to the identification of some genes frequently getting selected.