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

• Published in: BMC bioinformatics Vol. 15; no. 1; p. 49
• Main Authors: Chen, Kun-Huang, Wang, Kung-Jeng, Tsai, Min-Lung, Wang, Kung-Min, Adrian, Angelia Melani, Cheng, Wei-Chung, Yang, Tzu-Sen, Teng, Nai-Chia, Tan, Kuo-Pin, Chang, Ku-Shang
• Format: Journal Article
• Language: English
• Published: London BioMed Central 20.02.2014; BioMed Central Ltd
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Atoms & subatomic particles; Bayes Theorem; Bioinformatics; Biomedical and Life Sciences; Cancer; Cloning; Computational Biology - methods; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Databases, Factual; Decision Trees; Deoxyribonucleic acid; DNA; Female; Gene expression; Gene Expression Profiling - methods; Hospitals; Humans; Life Sciences; Male; Medical research; Methodology; Methodology Article; Microarrays; Neoplasms - classification; Neoplasms - genetics; Neoplasms - metabolism; Neural networks; Reproducibility of Results; Science; Statistical analysis; Statistical methods; Studies; Support Vector Machine; Transcriptome analysis; Tumors; Taiwan; Decision tree classifier; Gene expression; Particle swarm optimization; Cancer
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-15-49

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.