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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

ISBN: 978-989-758-015-4

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 classific ation 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. (More)

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Paper citation in several formats:
Lai, H.; Albrecht, A. and Steinhöfel, K. (2014). A Framework for High-throughput Gene Signatures with Microarray-based Brain Cancer Gene Expression Profiling Data.In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-015-4, pages 211-220. DOI: 10.5220/0004926002110220

@conference{icaart14,
author={Hung{-}Ming Lai. and Andreas Albrecht. and Kathleen Steinhöfel.},
title={A Framework for High-throughput Gene Signatures with Microarray-based Brain Cancer Gene Expression Profiling Data},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2014},
pages={211-220},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004926002110220},
isbn={978-989-758-015-4},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - A Framework for High-throughput Gene Signatures with Microarray-based Brain Cancer Gene Expression Profiling Data
SN - 978-989-758-015-4
AU - Lai, H.
AU - Albrecht, A.
AU - Steinhöfel, K.
PY - 2014
SP - 211
EP - 220
DO - 10.5220/0004926002110220

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