期刊
NEUROCOMPUTING
卷 165, 期 -, 页码 81-89出版社
ELSEVIER
DOI: 10.1016/j.neucom.2014.08.096
关键词
Pathway activity transformation; Multi-class classification; Lung cancer; Support Vector Machine; Multi layer perceptron; ANOVA
资金
- National Center for Genetic Engineering and Biotechnology, Thailand (BIOTEC)
- King Mongkut's University of Technology Thonburi
Pathway-based microarray analysis has been found to be a powerful tool to study disease mechanisms and to identify biological markers of complex diseases like lung cancer. From previous studies, the use of pathway activity transformed from gene expression data has been shown to be more informative in disease classification. However, current works on a pathway activity transformation method are for binary-class classification. In this study, we propose a pathway activity transformation method for multi-class data termed Analysis-of-Variance-based Feature Set (AFS). The classification results of using pathway activity derived from our proposed method show high classification power in three-fold cross-validation and robustness in across dataset validation for all four lung cancer datasets used. (C) 2015 Elsevier B.V. All rights reserved.
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