Apriori gene set-based microarray analysis for disease classification using unlabeled data
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Publication Details
Author list: Engchuan W., Chan J.H.
Publisher: Elsevier
Publication year: 2013
Volume number: 23
Start page: 137
End page: 145
Number of pages: 9
ISSN: 1877-0509
Languages: English-Great Britain (EN-GB)
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Abstract
Gene set-based microarray analysis allows researchers to better analyze the gene expression data for studying complex diseases like cancer. By transforming gene expression data into another form using gene set information, the biomarkers will have higher discriminative power and should result in more accurate disease classification. This work compares two techniques for applying our previously developed NCFS-i-based method to deal with unlabeled data, i.e. to make predictive diagnosis. Seven cancer datasets that include 4 breast cancer and 3 lung cancer datasets were used in this study. The results show that inferring gene set activity using curated phenotype-correlated genes (PCOGs) sets of training data is a more robust method for applying NCFS-i-based method to work with unlabeled data, providing biologically relevant gene sets. ฉ 2013 The Authors.
Keywords
Cancer classification, Feature Selection, Gene set activity, Microarray analysis
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