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Data Mining for Biologists

Data Mining for Biologists

Koji Tsuda
Copyright: © 2012 |Volume: 3 |Issue: 4 |Pages: 14
ISSN: 1947-9115|EISSN: 1947-9123|EISBN13: 9781466613249|DOI: 10.4018/ijkdb.2012100101
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MLA

Tsuda, Koji. "Data Mining for Biologists." IJKDB vol.3, no.4 2012: pp.1-14. http://doi.org/10.4018/ijkdb.2012100101

APA

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

Chicago

Tsuda, Koji. "Data Mining for Biologists," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 3, no.4: 1-14. http://doi.org/10.4018/ijkdb.2012100101

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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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