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Cross-Platform Analysis with Binarized Gene Expression Data

Cross-Platform Analysis with Binarized Gene Expression Data
Cross-Platform Analysis with Binarized Gene Expression Data
With widespread use of microarray technology as a potential diagnostics tool, the comparison of results obtained from the use of different platforms is of interest. When inference methods are designed using data collected using a particular platform, they are unlikely to work directly on measurements taken from a different type of array. We report on this cross-platform transfer problem, and show that working with transcriptome representations at binary numerical precision, similar to the gene expression bar code method, helps circumvent the variability across platforms in several cancer classification tasks. We compare our approach with a recent machine learning method specifically designed for shifting distributions, i.e., problems in which the training and testing data are not drawn from identical probability distributions, and show superior performance in three of the four problems in which we could directly compare.
978-3-642-04030-6
439-449
Tuna, Salih
10b3ffcd-3ed8-4bd5-987a-4d26946d685d
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Tuna, Salih
10b3ffcd-3ed8-4bd5-987a-4d26946d685d
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Tuna, Salih and Niranjan, Mahesan (2009) Cross-Platform Analysis with Binarized Gene Expression Data. Pattern Recognition in Bioinformatics, Sheffield. pp. 439-449 .

Record type: Conference or Workshop Item (Paper)

Abstract

With widespread use of microarray technology as a potential diagnostics tool, the comparison of results obtained from the use of different platforms is of interest. When inference methods are designed using data collected using a particular platform, they are unlikely to work directly on measurements taken from a different type of array. We report on this cross-platform transfer problem, and show that working with transcriptome representations at binary numerical precision, similar to the gene expression bar code method, helps circumvent the variability across platforms in several cancer classification tasks. We compare our approach with a recent machine learning method specifically designed for shifting distributions, i.e., problems in which the training and testing data are not drawn from identical probability distributions, and show superior performance in three of the four problems in which we could directly compare.

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More information

Published date: September 2009
Additional Information: Event Dates: September 2009
Venue - Dates: Pattern Recognition in Bioinformatics, Sheffield, 2009-09-01
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 268188
URI: http://eprints.soton.ac.uk/id/eprint/268188
ISBN: 978-3-642-04030-6
PURE UUID: ecd4ec68-b0a9-4ef7-b57b-f423b4b66023
ORCID for Mahesan Niranjan: ORCID iD orcid.org/0000-0001-7021-140X

Catalogue record

Date deposited: 11 Nov 2009 14:27
Last modified: 15 Mar 2024 03:29

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Contributors

Author: Salih Tuna
Author: Mahesan Niranjan ORCID iD

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