Show simple item record Clare, Amanda King, Ross Donald 2006-04-25T16:01:08Z 2006-04-25T16:01:08Z 2002
dc.identifier.citation Clare , A & King , R D 2002 , ' Machine learning of functional class from phenotype data ' Bioinformatics , vol 18 , no. 1 , pp. 160-166 . DOI: 10.1093/bioinformatics/18.1.160 en
dc.identifier.issn 1367-4803
dc.identifier.other PURE: 68590
dc.identifier.other PURE UUID: b0825448-8e7b-4fe4-b680-1ee5ab9e44af
dc.identifier.other dspace: 2160/160
dc.identifier.other DSpace_20121128.csv: row: 130
dc.identifier.other Scopus: 0036169141
dc.description Clare, A. and King R.D. (2002) Machine learning of functional class from phenotype data. Bioinformatics 18(1) 160-166 en
dc.description.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 en
dc.format.extent 7 en
dc.language.iso eng
dc.relation.ispartof Bioinformatics en
dc.rights en
dc.title Machine learning of functional class from phenotype data en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Bioinformatics and Computational Biology Group en
dc.description.status Peer reviewed en

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