Frank, E., Hall, M.A., Trigg, L., Holmes, G. and Witten, I.H. (2004). Data mining in bioinformatics using Weka. Bioinformatics, 20(15), 2479-2481.
Permanent Research Commons link: http://hdl.handle.net/10289/1295
The Weka machine learning workbench provides a general purpose environment for automatic classification, regression, clustering and feature selection-common data mining problems in bioinformatics research. It contains an extensive collection of machine learning algorithms and data exploration and the experimental comparison of different machine learning techniques on the same problem. Weka can process data given in the form of a single relational table. Its main objectives are to (a) assist users in extracting useful information from data and (b) enable them to easily identify a suitable algorithm for generating an accurate predictive model from it.
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This is an author’s version of an article published in the journal: Bioinformatics, (c) 2008 Oxford University Press. All Rights reserved.