Authors:
Hung-Ming Lai
1
;
Andreas Albrecht
2
and
Kathleen Steinhöfel
1
Affiliations:
1
King’s College London, United Kingdom
;
2
Middlesex University, United Kingdom
Keyword(s):
Brain Cancer, Feature Interdependence, Feature Selection, Gene Signature Selector, Microarray Data Analysis.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
Cancer classification through high-throughput gene expression profiles has been widely used in biomedical
research. Most recently, we portrayed a multivariate method for large scale gene selection based on
information theory with the central issue of feature interdependence, and we validated its effectiveness
using a colon cancer benchmark. The present paper further develops our previous work on feature
interdependence. Firstly, we have refined the method and proposed a complete framework to select a gene
signature for a certain disease phenotype prediction under high-throughput technologies. The framework has
then been applied to a brain cancer gene expression profile derived from Affymetrix Human Genome
U95Av2 Array, where the number of interrogated genes is six times larger than that in the previously
studied colon cancer data set. Three information theory based filters were used for comparison. Our
experimental results show that the framework outperforms them in terms of classifi
cation performance
based upon three performance measures. Additionally, to demonstrate how effectively feature
interdependence can be tackled within the framework, two sets of enrichment analysis have also been
performed. The results also show that more statistically significant gene sets and regulatory interactions
could be found in our gene signature. Therefore, this framework could be promising for high-throughput
gene selection around gene synergy.
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