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Bioinformatics Advance Access published online on January 19, 2007
Bioinformatics, 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 1,2, Martin Szugat 2 and Ralf Zimmer

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

1Corresponding author. Jan E. Gewehr, E-mail: support{at}bioweka.org, jan.gewehr{at}ifi.lmu.de


  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 visualisation 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.

2 Authors contributed equally to this work.

Associate Editor: Thomas Lengauer

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

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