Machine learning of functional class from phenotype data.
BACKGROUND
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 Open Reading Frames (ORFs) in Saccaromyces cerevisiae. Three sources of data were used: TRansposon-Insertion Phenotypes, Localization and Expression in Saccharomyces (TRIPLES), European Functional Analysis Network (EUROFAN) and Munich Information Center for Protein Sequences (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 > or = 80%.
Clare
Amanda
A
Department of Computer Science, University of Wales, Aberystwyth SY23 3DB, UK. ajc@aber.ac.uk
King
Ross D
RD
D001185:Artificial Intelligence; D019295:Computational Biology; D030541:Databases, Genetic; D009154:Mutation; D016366:Open Reading Frames; D010641:Phenotype; D012441:Saccharomyces cerevisiae / Q000235:genetics