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  Analyzing microarray data using quantitative association rules

Georgii, E., Richter, L., Rückert, U., & Kramer, S. (2005). Analyzing microarray data using quantitative association rules. Bioinformatics, 21(Supplement 2), 123-129.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D415-9 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-D781-7
Genre: Conference Paper

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 Creators:
Georgii, E1, Author              
Richter , L, Author
Rückert, U, Author
Kramer, S, Author
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: Motivation: We tackle the problem of finding regularities in microarray data. Various data mining tools, such as clustering, classification, Bayesian networks and association rules, have been applied so far to gain insight into gene-expression data. Association rule mining techniques used so far work on discretizations of the data and cannot account for cumulative effects. In this paper, we investigate the use of quantitative association rules that can operate directly on numeric data and represent cumulative effects of variables. Technically speaking, this type of quantitative association rules based on half-spaces can find non-axis-parallel regularities. Results: We performed a variety of experiments testing the utility of quantitative association rules for microarray data. First of all, the results should be statistically significant and robust against fluctuations in the data. Next, the approach should be scalable in the number of variables, which is important for such high-dimensional data. Finally, the rules should make sense biologically and be sufficiently different from rules found in regular association rule mining working with discretizations. In all of these dimensions, the proposed approach performed satisfactorily. Therefore, quantitative association rules based on half-spaces should be considered as a tool for the analysis of microarray gene-expression data.

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 Dates: 2005-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1093/bioinformatics/bti1121
BibTex Citekey: 4115
 Degree: -

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Title: Fourth European Conference on Computational Biology/Sixth Meeting of the Spanish Bioinformatics Network (ECCB/JBI 2005)
Place of Event: Madrid, Spain
Start-/End Date: 2005-09-28 - 2005-10-01

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Title: Bioinformatics
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 21 (Supplement 2) Sequence Number: - Start / End Page: 123 - 129 Identifier: ISSN: 1367-4803
CoNE: https://pure.mpg.de/cone/journals/resource/954926969991