期刊論文
| 學年 | 102 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2014-02-07 |
| 作品名稱 | Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm |
| 作品名稱（其他語言） | |
| 著者 | K.-H. Chen; K.-J. Wang; M.-L. Tsai; K.-M. Wang; A-M. Adrian; W.-C. Cheng; T.-S. Yang; N.-C. Teng; K.-P. Tan; K.-S. Chang |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | BMC Bioinformatics 15(49) |
| 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. |
| 關鍵字 | Gene expression;Cancer;Particle swarm optimization;Decision tree classifier |
| 語言 | en |
| ISSN | 1471-2105 |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | K.-H. Chen |
| 審稿制度 | 是 |
| 國別 | GBR |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/107030 ) |