Robust Signature Discovery for Affymetrix GeneChip

TY - CHAP

T1 - Robust Signature Discovery for Affymetrix GeneChip

AU - Lai, Hung-Ming

AU - Albrecht, Andreas

AU - Steinhöfel, Kathleen

PY - 2015/10

Y1 - 2015/10

N2 - Phenotype prediction is one of the central issues in genetics and medical sciences research. Due to the advent of highthroughput screening technologies, microarray-based cancer classification has become a standard procedure to identify cancer-related gene signatures. Since gene expression profiling in transcriptome is of high dimensionality, it is a challenging task to discover a biologically functional signature over different cell lines. In this article, we present an innovative framework for finding a small portion of discriminative genes for a specific disease phenotype classification by using information theory. The framework is a data-driven approach and considers feature relevance, redundancy, and interdependence in the context of feature pairs. Its effectiveness has been validated by using a brain cancer benchmark, where the gene expression profiling matrix is derived from Affymetrix Human Genome U95Av2 GeneChip®. Three multivariate filters based on information theory have also been used for comparison. To show the strengths of the framework, three performance measures, two sets of enrichment analysis, and a stability index have been used in our experiments. The results show that the framework is robust and able to discover a gene signature having a high level of classification performance and being more statistically significant enriched.

AB - Phenotype prediction is one of the central issues in genetics and medical sciences research. Due to the advent of highthroughput screening technologies, microarray-based cancer classification has become a standard procedure to identify cancer-related gene signatures. Since gene expression profiling in transcriptome is of high dimensionality, it is a challenging task to discover a biologically functional signature over different cell lines. In this article, we present an innovative framework for finding a small portion of discriminative genes for a specific disease phenotype classification by using information theory. The framework is a data-driven approach and considers feature relevance, redundancy, and interdependence in the context of feature pairs. Its effectiveness has been validated by using a brain cancer benchmark, where the gene expression profiling matrix is derived from Affymetrix Human Genome U95Av2 GeneChip®. Three multivariate filters based on information theory have also been used for comparison. To show the strengths of the framework, three performance measures, two sets of enrichment analysis, and a stability index have been used in our experiments. The results show that the framework is robust and able to discover a gene signature having a high level of classification performance and being more statistically significant enriched.

U2 - 10.1007/978-3-319-25210-0_20

DO - 10.1007/978-3-319-25210-0_20

M3 - Chapter

SN - 9783319252094

VL - 8946

SP - 329

EP - 345

BT - Lecture Notes in Artificial Intelligence

A2 - Duval, Béatrice

A2 - van den Herik, Jaap

A2 - Loiseau, Stephane

A2 - Filipe, Joaquim

PB - Springer

ER -