Analysis of tiling microarray data by learning vector quantization and relevance learning

Michael Biehl; Rainer Breitling; Yang Li

doi:10.1007/978-3-540-77226-2_88

Analysis of tiling microarray data by learning vector quantization and relevance learning

Michael Biehl^*, Rainer Breitling, Yang Li

^*Corresponding author for this work

Intelligent Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

8 Citations (Scopus)

342 Downloads (Pure)

Abstract

We apply learning vector quantization to the analysis of tiling microarray data. As an example we consider the classification of C. elegans genomic probes as intronic or exonic. Training is based on the current annotation of the genome. Relevance learning techniques are used to weight and select features according to their importance for the classification. Among other findings, the analysis suggests that correlations between the perfect match intensity of a particular probe and its neighbors are highly relevant for successful exon identification.

Original language	English
Title of host publication	INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2007
Editors	H Yin, P Tino, E Corchado, W Byrne, Yao
Place of Publication	BERLIN
Publisher	Springer
Pages	880-889
Number of pages	10
ISBN (Print)	978-3-540-77225-5
DOIs	https://doi.org/10.1007/978-3-540-77226-2_88
Publication status	Published - 2007
Event	8th International Conference on Intelligent Data Engineering and Automated Learning - Duration: 16-Dec-2007 → 19-Dec-2007

Publication series

Name	LECTURE NOTES IN COMPUTER SCIENCE
Publisher	SPRINGER-VERLAG BERLIN
Volume	4881
ISSN (Print)	0302-9743

Other

Other	8th International Conference on Intelligent Data Engineering and Automated Learning
Period	16/12/2007 → 19/12/2007

Keywords

GENOME

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10.1007/978-3-540-77226-2_88

2007LNCSBiehl.pdfFinal publisher's version, 430 KB

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Biehl, M., Breitling, R., & Li, Y. (2007). Analysis of tiling microarray data by learning vector quantization and relevance learning. In H. Yin, P. Tino, E. Corchado, W. Byrne, & Yao (Eds.), INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2007 (pp. 880-889). (LECTURE NOTES IN COMPUTER SCIENCE; Vol. 4881). Springer. https://doi.org/10.1007/978-3-540-77226-2_88

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title = "Analysis of tiling microarray data by learning vector quantization and relevance learning",

abstract = "We apply learning vector quantization to the analysis of tiling microarray data. As an example we consider the classification of C. elegans genomic probes as intronic or exonic. Training is based on the current annotation of the genome. Relevance learning techniques are used to weight and select features according to their importance for the classification. Among other findings, the analysis suggests that correlations between the perfect match intensity of a particular probe and its neighbors are highly relevant for successful exon identification.",

keywords = "GENOME",

author = "Michael Biehl and Rainer Breitling and Yang Li",

note = "Relation: http://www.rug.nl/gbb/ date_submitted:2009 Rights: University of Groningen, Groningen Biomolecular Sciences and Biotechnology Institute; 8th International Conference on Intelligent Data Engineering and Automated Learning ; Conference date: 16-12-2007 Through 19-12-2007",

year = "2007",

doi = "10.1007/978-3-540-77226-2_88",

language = "English",

isbn = "978-3-540-77225-5",

series = "LECTURE NOTES IN COMPUTER SCIENCE",

publisher = "Springer",

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editor = "H Yin and P Tino and E Corchado and W Byrne and Yao",

booktitle = "INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2007",

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Biehl, M, Breitling, R & Li, Y 2007, Analysis of tiling microarray data by learning vector quantization and relevance learning. in H Yin, P Tino, E Corchado, W Byrne & Yao (eds), INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2007. LECTURE NOTES IN COMPUTER SCIENCE, vol. 4881, Springer, BERLIN, pp. 880-889, 8th International Conference on Intelligent Data Engineering and Automated Learning, 16/12/2007. https://doi.org/10.1007/978-3-540-77226-2_88

Analysis of tiling microarray data by learning vector quantization and relevance learning. / Biehl, Michael; Breitling, Rainer; Li, Yang.
INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2007. ed. / H Yin; P Tino; E Corchado; W Byrne; Yao. BERLIN: Springer, 2007. p. 880-889 (LECTURE NOTES IN COMPUTER SCIENCE; Vol. 4881).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Analysis of tiling microarray data by learning vector quantization and relevance learning

AU - Biehl, Michael

AU - Breitling, Rainer

AU - Li, Yang

N1 - Relation: http://www.rug.nl/gbb/ date_submitted:2009 Rights: University of Groningen, Groningen Biomolecular Sciences and Biotechnology Institute

PY - 2007

Y1 - 2007

N2 - We apply learning vector quantization to the analysis of tiling microarray data. As an example we consider the classification of C. elegans genomic probes as intronic or exonic. Training is based on the current annotation of the genome. Relevance learning techniques are used to weight and select features according to their importance for the classification. Among other findings, the analysis suggests that correlations between the perfect match intensity of a particular probe and its neighbors are highly relevant for successful exon identification.

AB - We apply learning vector quantization to the analysis of tiling microarray data. As an example we consider the classification of C. elegans genomic probes as intronic or exonic. Training is based on the current annotation of the genome. Relevance learning techniques are used to weight and select features according to their importance for the classification. Among other findings, the analysis suggests that correlations between the perfect match intensity of a particular probe and its neighbors are highly relevant for successful exon identification.

KW - GENOME

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DO - 10.1007/978-3-540-77226-2_88

M3 - Conference contribution

SN - 978-3-540-77225-5

T3 - LECTURE NOTES IN COMPUTER SCIENCE

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EP - 889

BT - INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2007

A2 - Yin, H

A2 - Tino, P

A2 - Corchado, E

A2 - Byrne, W

A2 - Yao, null

PB - Springer

CY - BERLIN

T2 - 8th International Conference on Intelligent Data Engineering and Automated Learning

Y2 - 16 December 2007 through 19 December 2007

ER -

Analysis of tiling microarray data by learning vector quantization and relevance learning

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