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A novel combined ICA and clustering technique for the classification of gene expression data

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conference contribution
posted on 14.12.2009 by Amrish Kapoor, Thomas Bowles, Jonathon Chambers
This study presents an effective method of blindly classifying large amounts of gene expression data into biologically meaningful groups using a combination of independent component analysis (ICA) and clustering techniques. Specifically, we show that the genes can be classified blindly into several groups based solely on their expression profiles. These groups have a very close correspondence with benchmarks obtained by studies using domain knowledge. These results suggest that ICA can be a very useful pre-processing tool in blind gene classification, rather than using the resulting sources as the final model profiles.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Citation

KAPOOR, A., BOWLES, T. and CHAMBERS, J.A., 2005. A combined ICA and clustering technique for the classification of gene expression data. IN: Proceedings of 2005 International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005), Philadelphia, Pennsylvania, USA, 18-23 March, Vol.5, pp.621-624.

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© IEEE

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VoR (Version of Record)

Publication date

2005

Notes

This is a conference paper [© IEEE]. It is also available at: http://ieeexplore.ieee.org/ Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

ISBN

0780388747

Language

en

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