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Knowledge discovery in multi-label phenotype data. (English) Zbl 1009.68730

De Raedt, Luc (ed.) et al., Principles of data mining and knowledge discovery. 5th European conference, PKDD 2001, Freiburg, Germany, September 3-5, 2001. Proceedings. Dedicated to Jan Żytkow. Berlin: Springer. Lect. Notes Comput. Sci. 2168, 42-53 (2001).
Summary: The biological sciences are undergoing an explosion in the amount of available data. New data analysis methods are needed to deal with the data. We present work using KDD to analyse data from mutant phenotype growth experiments with the yeast S. cerevisiae to predict novel gene functions. The analysis of the data presented a number of challenges: multi-class labels, a large number of sparsely populated classes, the need to learn a set of accurate rules (not a complete classification), and a very large amount of missing values. We developed resampling strategies and modified the algorithm C4.5 to deal with these problems. Rules were learnt which are accurate and biologically meaningful. The rules predict function of 83 putative genes of currently unknown function at an estimated accuracy of \(\geq 80\%\).
For the entire collection see [Zbl 0972.68686].

MSC:

68U99 Computing methodologies and applications
68P20 Information storage and retrieval of data
68P15 Database theory
92C55 Biomedical imaging and signal processing

Software:

C4.5; UCI-ml; BoosTexter
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