Effect of Background Correction on Cancer Classification with Gene Expression Data

Authors:
Adelaide Freitas;Gladys Castillo;Ana São Marcos
Affiliations:
Department of Mathematics, University of Aveiro, Portugal;Department of Mathematics, University of Aveiro, Portugal;Department of Mathematics, University of Aveiro, Portugal
Venue:
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
Year:
2009

Citing 2
Cited 0

YALE: rapid prototyping for complex data mining tasks

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A comparison of background correction methods for two-colour microarrays

Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper empirically compares six background correction methods aimed at removing unspecific background noise of the overall signal level measured by a scanner across microarrays. Using three published cDNA microarray datasets we investigated the effect of background correction on cancer classification in terms of the predictive performance of two classifiers (k-NN and support vector machine with linear kernel) induced from microarray data where a particular background correction method is applied, individually and in combination with a single-bias or double-bias-removal normalization method.