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  Data Mining for Biologists

Tsuda, K. (2012). Data Mining for Biologists. International Journal of Knowledge Discovery in Bioinformatics, 3(4), 1-14. doi:10.4018/ijkdb.2012100101.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0001-8492-4 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-8493-3
Genre: Journal Article

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 Creators:
Tsuda, K1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: In this tutorial article, the author reviews basics about frequent pattern mining algorithms, including itemset mining, association rule mining, and graph mining. These algorithms can find frequently appearing substructures in discrete data. They can discover structural motifs, for example, from mutation data, protein structures, and chemical compounds. As they have been primarily used for business data, biological applications are not so common yet, but their potential impact would be large. Recent advances in computers including multicore machines and ever increasing memory capacity support the application of such methods to larger datasets. The author explains technical aspects of the algorithms, but do not go into details. Current biological applications are summarized and possible future directions are given.

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 Dates: 2012-10
 Publication Status: Published in print
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 Identifiers: DOI: 10.4018/ijkdb.2012100101
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Title: International Journal of Knowledge Discovery in Bioinformatics
Source Genre: Journal
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Pages: - Volume / Issue: 3 (4) Sequence Number: - Start / End Page: 1 - 14 Identifier: -