Pathway activity transformation for multi-class classification of lung cancer datasets
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Publication Details
Author list: Engchuan W., Chan J.H.
Publisher: Elsevier
Publication year: 2015
Journal: Neurocomputing (0925-2312)
Volume number: 165
Start page: 81
End page: 89
Number of pages: 9
ISSN: 0925-2312
eISSN: 1872-8286
Languages: English-Great Britain (EN-GB)
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Abstract
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. ฉ 2015 Elsevier B.V.
Keywords
multilayer perceptron, Pathway activity transformation