Gene selection for cancer identification: A decision tree model empowered by particle swarm optimization algorithm

Kun Huang Chen, Kung Jeng Wang, Min Lung Tsai, Kung Min Wang, Angelia M. Adrian, Wei Chung Cheng, Tzu Sen Yang, Nai Chia Teng, Kuo Pin Tan, Ku Shang Chang

Research output: Contribution to journalArticlepeer-review

116 Citations (Scopus)


Background: In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data.Results: To achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods (support vector machine, self-organizing map, back propagation neural network, C4.5 decision tree, Naive Bayes, CART decision tree, and artificial immune recognition system) and conducts experiments on 11 gene expression cancer datasets.Conclusion: Based on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification.

Original languageEnglish
Article number49
JournalBMC Bioinformatics
Issue number1
Publication statusPublished - Feb 20 2014


  • Cancer
  • Decision tree classifier
  • Gene expression
  • Particle swarm optimization

ASJC Scopus subject areas

  • Applied Mathematics
  • Molecular Biology
  • Structural Biology
  • Biochemistry
  • Computer Science Applications


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