Microarray design using the Hilbert-Schmidt independence criterion

Justin Bedo

doi:10.1007/978-3-540-88436-1_25

Microarray design using the Hilbert-Schmidt independence criterion

Justin Bedo^*

^*Corresponding author for this work

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

6 Citations (Scopus)

Abstract

This paper explores the design problem of selecting a small subset of clones from a large pool for creation of a microarray plate. A new kernel based unsupervised feature selection method using the Hilbert-Schmidt independence criterion (hsic) is presented and evaluated on three microarray datasets: the Alon colon cancer dataset, the van 't Veer breast cancer dataset, and a multiclass cancer of unknown primary dataset. The experiments show that subsets selected by the hsic resulted in equivalent or better performance than supervised feature selection, with the added benefit that the subsets are not target specific.

Original language	English
Title of host publication	3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008
Publisher	Springer Verlag
Pages	288-298
Number of pages	11
ISBN (Print)	3540884343, 9783540884347
DOIs	https://doi.org/10.1007/978-3-540-88436-1_25
Publication status	Published - 2008
Event	3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008 - Melbourne, VIC, Australia Duration: 15 Oct 2008 → 17 Oct 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5265 LNBI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008
Country/Territory	Australia
City	Melbourne, VIC
Period	15/10/08 → 17/10/08

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10.1007/978-3-540-88436-1_25

Cite this

Bedo, J. (2008). Microarray design using the Hilbert-Schmidt independence criterion. In 3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008 (pp. 288-298). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5265 LNBI). Springer Verlag. https://doi.org/10.1007/978-3-540-88436-1_25

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title = "Microarray design using the Hilbert-Schmidt independence criterion",

abstract = "This paper explores the design problem of selecting a small subset of clones from a large pool for creation of a microarray plate. A new kernel based unsupervised feature selection method using the Hilbert-Schmidt independence criterion (hsic) is presented and evaluated on three microarray datasets: the Alon colon cancer dataset, the van 't Veer breast cancer dataset, and a multiclass cancer of unknown primary dataset. The experiments show that subsets selected by the hsic resulted in equivalent or better performance than supervised feature selection, with the added benefit that the subsets are not target specific.",

author = "Justin Bedo",

year = "2008",

doi = "10.1007/978-3-540-88436-1_25",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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pages = "288--298",

booktitle = "3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008",

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note = "3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008 ; Conference date: 15-10-2008 Through 17-10-2008",

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Bedo, J 2008, Microarray design using the Hilbert-Schmidt independence criterion. in 3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5265 LNBI, Springer Verlag, pp. 288-298, 3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008, Melbourne, VIC, Australia, 15/10/08. https://doi.org/10.1007/978-3-540-88436-1_25

Microarray design using the Hilbert-Schmidt independence criterion. / Bedo, Justin.
3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008. Springer Verlag, 2008. p. 288-298 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5265 LNBI).

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

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AB - This paper explores the design problem of selecting a small subset of clones from a large pool for creation of a microarray plate. A new kernel based unsupervised feature selection method using the Hilbert-Schmidt independence criterion (hsic) is presented and evaluated on three microarray datasets: the Alon colon cancer dataset, the van 't Veer breast cancer dataset, and a multiclass cancer of unknown primary dataset. The experiments show that subsets selected by the hsic resulted in equivalent or better performance than supervised feature selection, with the added benefit that the subsets are not target specific.

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SN - 9783540884347

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Bedo J. Microarray design using the Hilbert-Schmidt independence criterion. In 3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008. Springer Verlag. 2008. p. 288-298. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-88436-1_25

Microarray design using the Hilbert-Schmidt independence criterion

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