Effect of pre-processing methods on microarray-based SVM classifiers in affymetrix genechips

Affymetrix High Oligonucleotide expression arrays are widely used for the high-throughput assessment of gene expression of thousands of genes simultaneously. Although disputed by several authors, there are non-biological variations and systematic biases that must be removed as much as possible throu...

Full description

Saved in:
Bibliographic Details
Main Authors: Florido, J P, Pomares, H, Rojas, I, Urquiza, J M, Herrera, L J, Claros, M G
Format: Conference Proceeding
Language:English
Subjects:
Cancer
Gene expression
Kernel
Polynomials
Probes
Support vector machines
Tumors
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Affymetrix High Oligonucleotide expression arrays are widely used for the high-throughput assessment of gene expression of thousands of genes simultaneously. Although disputed by several authors, there are non-biological variations and systematic biases that must be removed as much as possible through the pre-processing step before an absolute expression level for every gene is assessed. It is important to evaluate microarray pre-processing procedures not only to the detection of differentially expressed genes, but also to classification, since a major use of microarrays is the expression-based phenotype classification. Thus, in this paper, we use several cancer microarray datasets to assess the influence of five different pre-processing methods in Support Vector Machine-based classification methodologies with different kernels: linear, Radial Basis Functions (RBFs) and polynomial.
ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2010.5596308