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

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• 作者：Kun-Huang Chen (1)
  Kung-Jeng Wang (1)
  Min-Lung Tsai (2)
  Kung-Min Wang (3)
  Angelia Melani Adrian (1)
  Wei-Chung Cheng (4) (5)
  Tzu-Sen Yang (6) (7)
  Nai-Chia Teng (8)
  Kuo-Pin Tan (9)
  Ku-Shang Chang (2)
  
• 关键词：Gene expression ; Cancer ; Particle swarm optimization ; Decision tree classifier
• 刊名：BMC Bioinformatics
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：15
• 期：1
• 全文大小：409 KB
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• 作者单位：Kun-Huang Chen (1)
  Kung-Jeng Wang (1)
  Min-Lung Tsai (2)
  Kung-Min Wang (3)
  Angelia Melani Adrian (1)
  Wei-Chung Cheng (4) (5)
  Tzu-Sen Yang (6) (7)
  Nai-Chia Teng (8)
  Kuo-Pin Tan (9)
  Ku-Shang Chang (2)
  
  1. Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, 106, Taiwan, R.O.C
  2. Department of Food Science, Yuanpei University, No. 306, Yuanpei Street, Hsinchu, 300, Taiwan, R.O.C
  3. Department of Surgery, Shin-Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan, R.O.C
  4. Pediatric Neurosurgery, Department of Surgery, Cheng Hsin General Hospital, Taipei, 11220, Taiwan, R.O.C
  5. Genomic Research Center, National Yang-Ming University, Taipei, 11221, Taiwan, R.O.C
  6. School of Dental Technology, Taipei Medical University, Taipei, 110, Taiwan, R.O.C
  7. Taiwan Research Center for Biomedical Implants and Microsurgery Devices, Taipei Medical University, Taipei, 110, Taiwan, R.O.C
  8. School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan, R.O.C
  9. MBA, School of Management, National Taiwan University of Science and Technology, Taipei, 106, Taiwan, R.O.C
  
• ISSN：1471-2105

文摘

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.