Data shuffling and statistical analysis on microarray data for gene selection: a comparative study on filtering methods. (18th March 2011)

• Record Type: Journal Article
• Authors: Ding, Zejin; Zhang, Yan-Qing; Zhao, Yichuan
• Is Part Of: International journal of functional informatics and personalised medicine. Volume 3:Number 3(2010)
• Page Start: 183
• Page End: 203
• Publication Date: 2011-03-18
• Subjects: gene selection -- feature selection -- microarray data -- statistical analysis -- data shuffling -- SVM -- support vector machines -- filtering methods; Biomedical engineering -- Periodicals; Bioinformatics -- Periodicals; 610.28
• Languages: English
• ISSNs: 1756-2104

Abstract

Computational analysis have been broadly used to discover disease-relevant genes from microarray expression data. In this paper, we extend a traditional statistical metric to a second level to measure gene-disease relations, testing such relation whether can be replicated by randomly shuffling the gene expression data. The traditional metric can be considered as a first-level metric; the relevance of each gene is then verified through the second-level significance testing based on the first-level metric calculated on the original data and shuffled data. We show that this method can also produce high classification performance, compared with other filter-based methods.