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Title: A novel combined ICA and clustering technique for the classification of gene expression data
Authors: Kapoor, Amrish
Bowles, Thomas
Chambers, Jonathon
Issue Date: 2005
Publisher: © IEEE
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.
Abstract: 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.
Description: 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.
Version: Published
DOI: 10.1109/ICASSP.2005.1416380
URI: https://dspace.lboro.ac.uk/2134/5665
ISBN: 0780388747
Appears in Collections:Conference Papers and Contributions (Mechanical, Electrical and Manufacturing Engineering)

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