Strategies for winnowing microarray data

David B. Skillicorn; Simeon J. Simoff; Paul J. Kennedy; Daniel R. Catchpoole; Michael W. Berry

Strategies for winnowing microarray data

David B. Skillicorn, Simeon J. Simoff, Paul J. Kennedy, Daniel R. Catchpoole, Michael W. Berry

Research output: Chapter in Book / Conference Paper › Conference Paper

Abstract

The analysis of microarray datasets is complicated by the magnitude of the available information. Most data mining techniques are signifcantly hampered by irrelevant or redundant information. Hence it is useful to reduce datasets to manageable size by discarding such useless information. We present techniques for winnowing microarray datasets using singular value decomposition and semidiscrete decomposition, and show how they can be tuned to extract some information about the internal correlative structure of large datasets.

Original language	English
Title of host publication	Proceedings of the SIAM Bioinformatics Workshop 2004, held in conjunction with the Fourth SIAM International Conference on Data Mining, in Florida, USA, on 24 April, 2004
Publisher	Society for Industrial and Applied Mathematics
Number of pages	10
ISBN (Print)	0898715687
Publication status	Published - 2004
Event	SIAM Bioinformatics Workshop - Duration: 1 Jan 2004 → …

Conference

Conference	SIAM Bioinformatics Workshop
Period	1/01/04 → …

Keywords

data mining
information storage and retrieval systems
microarrays
datasets
analysis

Cite this

@inproceedings{d0f54f581fdf42f2aedea4df916bc025,

title = "Strategies for winnowing microarray data",

abstract = "The analysis of microarray datasets is complicated by the magnitude of the available information. Most data mining techniques are signifcantly hampered by irrelevant or redundant information. Hence it is useful to reduce datasets to manageable size by discarding such useless information. We present techniques for winnowing microarray datasets using singular value decomposition and semidiscrete decomposition, and show how they can be tuned to extract some information about the internal correlative structure of large datasets.",

keywords = "data mining, information storage and retrieval systems, microarrays, datasets, analysis",

author = "Skillicorn, {David B.} and Simoff, {Simeon J.} and Kennedy, {Paul J.} and Catchpoole, {Daniel R.} and Berry, {Michael W.}",

year = "2004",

language = "English",

isbn = "0898715687",

booktitle = "Proceedings of the SIAM Bioinformatics Workshop 2004, held in conjunction with the Fourth SIAM International Conference on Data Mining, in Florida, USA, on 24 April, 2004",

publisher = "Society for Industrial and Applied Mathematics",

note = "SIAM Bioinformatics Workshop ; Conference date: 01-01-2004",

}

Skillicorn, DB, Simoff, SJ, Kennedy, PJ, Catchpoole, DR & Berry, MW 2004, Strategies for winnowing microarray data. in Proceedings of the SIAM Bioinformatics Workshop 2004, held in conjunction with the Fourth SIAM International Conference on Data Mining, in Florida, USA, on 24 April, 2004. Society for Industrial and Applied Mathematics, SIAM Bioinformatics Workshop, 1/01/04.

Strategies for winnowing microarray data. / Skillicorn, David B.; Simoff, Simeon J.; Kennedy, Paul J. et al.
Proceedings of the SIAM Bioinformatics Workshop 2004, held in conjunction with the Fourth SIAM International Conference on Data Mining, in Florida, USA, on 24 April, 2004. Society for Industrial and Applied Mathematics, 2004.

Research output: Chapter in Book / Conference Paper › Conference Paper

TY - GEN

T1 - Strategies for winnowing microarray data

AU - Skillicorn, David B.

AU - Simoff, Simeon J.

AU - Kennedy, Paul J.

AU - Catchpoole, Daniel R.

AU - Berry, Michael W.

PY - 2004

Y1 - 2004

N2 - The analysis of microarray datasets is complicated by the magnitude of the available information. Most data mining techniques are signifcantly hampered by irrelevant or redundant information. Hence it is useful to reduce datasets to manageable size by discarding such useless information. We present techniques for winnowing microarray datasets using singular value decomposition and semidiscrete decomposition, and show how they can be tuned to extract some information about the internal correlative structure of large datasets.

AB - The analysis of microarray datasets is complicated by the magnitude of the available information. Most data mining techniques are signifcantly hampered by irrelevant or redundant information. Hence it is useful to reduce datasets to manageable size by discarding such useless information. We present techniques for winnowing microarray datasets using singular value decomposition and semidiscrete decomposition, and show how they can be tuned to extract some information about the internal correlative structure of large datasets.

KW - data mining

KW - information storage and retrieval systems

KW - microarrays

KW - datasets

KW - analysis

UR - http://handle.uws.edu.au:8081/1959.7/45321

M3 - Conference Paper

SN - 0898715687

BT - Proceedings of the SIAM Bioinformatics Workshop 2004, held in conjunction with the Fourth SIAM International Conference on Data Mining, in Florida, USA, on 24 April, 2004

PB - Society for Industrial and Applied Mathematics

T2 - SIAM Bioinformatics Workshop

Y2 - 1 January 2004

ER -

Strategies for winnowing microarray data

Abstract

Conference

Keywords

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