Machine learning of functional class from phenotype data

Amanda Clare, Ross Donald King

Research output: Contribution to journalArticlepeer-review

50 Citations (SciVal)

Abstract

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/.
Original languageEnglish
Pages (from-to)160-166
Number of pages7
JournalBioinformatics
Volume18
Issue number1
DOIs
Publication statusPublished - 2002

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