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

研究成果: 雜誌貢獻文章

71 引文 斯高帕斯(Scopus)

摘要

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.
原文英語
文章編號49
期刊BMC Bioinformatics
15
發行號1
DOIs
出版狀態已發佈 - 二月 20 2014

ASJC Scopus subject areas

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

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    Chen, K. H., Wang, K. J., Tsai, M. L., Wang, K. M., Adrian, A. M., Cheng, W. C., Yang, T. S., Teng, N. C., Tan, K. P., & Chang, K. S. (2014). Gene selection for cancer identification: A decision tree model empowered by particle swarm optimization algorithm. BMC Bioinformatics, 15(1), [49]. https://doi.org/10.1186/1471-2105-15-49