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Bioinformatics Advance Access originally published online on January 19, 2007
Bioinformatics 2007 23(5):651-653; doi:10.1093/bioinformatics/btl671
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

BioWeka—extending the Weka framework for bioinformatics

Jan E. Gewehr *,{dagger}, Martin Szugat {dagger} and Ralf Zimmer

Practical Informatics and Bioinformatics Group, Department of Informatics, Ludwig-Maximilians-University Munich, Amalienstrasse 17, D-80333 Munich, Germany

*To whom correspondence should be addressed.


  Abstract

Summary: Given the growing amount of biological data, data mining methods have become an integral part of bioinformatics research. Unfortunately, standard data mining tools are often not sufficiently equipped for handling raw data such as e.g. amino acid sequences. One popular and freely available framework that contains many well-known data mining algorithms is the Waikato Environment for Knowledge Analysis (Weka). In the BioWeka project, we introduce various input formats for bioinformatics data and bioinformatics methods like alignments to Weka. This allows users to easily combine them with Weka's classification, clustering, validation and visualization facilities on a single platform and therefore reduces the overhead of converting data between different data formats as well as the need to write custom evaluation procedures that can deal with many different programs. We encourage users to participate in this project by adding their own components and data formats to BioWeka.

Availability: The software, documentation and tutorial are available at http://www.bioweka.org.

Contact: support{at}bioweka.org

{dagger}The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.

Received on September 14, 2006; revised on November 25, 2006; accepted on January 3, 2007

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