Multiclass microarray data classification using GA/ANN method

Authors:
Tsun-Chen Lin;Ru-Sheng Liu;Ya-Ting Chao;Shu-Yuan Chen
Affiliations:
Department of Computer Science and Engineering, Yuan Ze University, Nei-Li, Chung-Li, Taoyuan, Taiwan, ROC;Department of Computer Science and Engineering, Yuan Ze University, Nei-Li, Chung-Li, Taoyuan, Taiwan, ROC;Graduate School of Biotechnology and Bioinformatics, Yuan Ze University, Nei-Li, Chung-Li, Taoyuan, Taiwan, ROC;Department of Computer Science and Engineering, Yuan Ze University, Nei-Li, Chung-Li, Taoyuan, Taiwan, ROC
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

Citing 2
Cited 2

ICGA-PSO-ELM Approach for Accurate Multiclass Cancer Classification Resulting in Reduced Gene Sets in Which Genes Encoding Secreted Proteins Are Highly Represented

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Hybrid genetic algorithm-neural network: Feature extraction for unpreprocessed microarray data

Artificial Intelligence in Medicine

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Abstract

This work aims to explore the use of gene expression data in discriminating heterogeneous cancers. We introduce hybrid learning methodology that integrates genetic algorithms (GA) and artificial neural networks (ANN) to find optimal subsets of genes for tissue/cancer classification. This method was tested on two published microarray datasets: (1) NCI60 cancer cell lines and (2) the GCM dataset. Experimental results on classifying both datasets show that our GA/ANN method not only outperformed many reported prediction approaches, but also reduced the number of predictive genes needed in classification analysis.