title: Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm.
abstract: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan, R,O,C. khchen@mail.ntust.edu.tw.
authors.forename: Kun-Huang
authors.initials: KH
authors.lastname: Chen
authors.forename: Kung-Jeng
authors.initials: KJ
authors.lastname: Wang
authors.forename: Min-Lung
authors.initials: ML
authors.lastname: Tsai
authors.forename: Kung-Min
authors.initials: KM
authors.lastname: Wang
authors.forename: Angelia Melani
authors.initials: AM
authors.lastname: Adrian
authors.forename: Wei-Chung
authors.initials: WC
authors.lastname: Cheng
authors.forename: Tzu-Sen
authors.initials: TS
authors.lastname: Yang
authors.forename: Nai-Chia
authors.initials: NC
authors.lastname: Teng
authors.forename: Kuo-Pin
authors.initials: KP
authors.lastname: Tan
authors.forename: Ku-Shang
authors.initials: KS
authors.lastname: Chang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D003663:Decision Trees; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008297:Male; D009369:Neoplasms / Q000145:classification / Q000235:genetics* / Q000378:metabolism; D015203:Reproducibility of Results; D060388:Support Vector Machine