Abstract - Machine learning of functional class from phenotype data
Amanda Clare and Ross D. King
Bioinformatics 18(1) 2002 pp 160-166
Motivation:
Mutant phenotype growth experiments are an important
novel source of functional genomics data which have received little
attention in bioinformatics. We applied supervised machine learning
to the problem of using phenotype data to predict the functional class
of ORFs in S. cerevisiae. Three sources of data were used:
TRIPLES, EUROFAN and MIPS. The analysis of the data presented a
number of challenges to machine learning: 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 modified the algorithm C4.5 to deal with
these problems.
Results:
Rules were learnt which are accurate and biologically
meaningful. The rules predict function of 83 ORFs of unknown
function at an estimated accuracy of >= 80% .
Availability:
The data and complete results are available at
http://www.aber.ac.uk/compsci/Research/bio/dss/phenotype/.
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