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  Integrating Structured Biological data by Kernel Maximum Mean Discrepancy

Borgwardt, K., Gretton, A., Rasch, M., Kriegel, H.-P., Schölkopf, B., & Smola, A. (2006). Integrating Structured Biological data by Kernel Maximum Mean Discrepancy. Bioinformatics, 22(14), e49-e57.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D08B-1 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-9A20-A
Genre: Conference Paper

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 Creators:
Borgwardt, KM, Author              
Gretton, A1, 2, Author              
Rasch, M, Author              
Kriegel, H-P, Author
Schölkopf, B1, 2, Author              
Smola, A, Author              
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: Motivation: Many problems in data integration in bioinformatics can be posed as one common question: Are two sets of observations generated by the same distribution? We propose a kernel-based statistical test for this problem, based on the fact that two distributions are different if and only if there exists at least one function having different expectation on the two distributions. Consequently we use the maximum discrepancy between function means as the basis of a test statistic. The Maximum Mean Discrepancy (MMD) can take advantage of the kernel trick, which allows us to apply it not only to vectors, but strings, sequences, graphs, and other common structured data types arising in molecular biology. Results: We study the practical feasibility of an MMD-based test on three central data integration tasks: Testing cross-platform comparability of microarray data, cancer diagnosis, and data-content based schema matching for two different protein function classification schemas. In all of these experiments, including high-dimensional ones, MMD is very accurate in finding samples that were generated from the same distribution, and outperforms its best competitors. Conclusions: We have defined a novel statistical test of whether two samples are from the same distribution, compatible with both multivariate and structured data, that is fast, easy to implement, and works well, as confirmed by our experiments.

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 Dates: 2006-07
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1093/bioinformatics/btl242
BibTex Citekey: 3981
 Degree: -

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Title: 14th International Conference on Intelligent Systems for Molecular Biology (ISMB 2006)
Place of Event: Fortaleza, Brazil
Start-/End Date: 2006-08-06 - 2006-08-10

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Title: Bioinformatics
Source Genre: Journal
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Affiliations:
Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 22 (14) Sequence Number: - Start / End Page: e49 - e57 Identifier: ISSN: 1367-4803
CoNE: https://pure.mpg.de/cone/journals/resource/954926969991