Show simple item record King, Ross Donald Clare, Amanda 2006-04-24T15:26:10Z 2006-04-24T15:26:10Z 2003
dc.identifier.citation King , R D & Clare , A 2003 , ' Predicting gene function in Saccharomyces cerevisiae ' Bioinformatics , vol 19 , no. S2 , pp. 42-49 . DOI: 10.1093/bioinformatics/btg1058 en
dc.identifier.issn 1367-4803
dc.identifier.other PURE: 68068
dc.identifier.other PURE UUID: 2997a716-41c1-48cc-8d0e-355621f7acd8
dc.identifier.other dspace: 2160/129
dc.identifier.other DSpace_20121128.csv: row: 104
dc.identifier.other Scopus: 6944251719
dc.description Clare, A. and King R.D. (2003) Predicting gene function in Saccharomyces cerevisiae. 2nd European Conference on Computational Biology (ECCB '03). (published as a journal supplement in Bioinformatics 19: ii42-ii49) en
dc.description.abstract Motivation S. cerevisiae is one of the most important model organisms, and has has been the focus of over a century of study. In spite of these efforts, 40% of its open reading frames (ORFs) remain classified as having unknown function (MIPS: Munich Information Center for Protein Sequences). We wished to make predictions for the function of these ORFs using data mining, as we have previously successfully done for the genomes of M. tuberculosis and E. coli. Applying this approach to the larger and eukaryotic S. cerevisiae genome involves modifying the machine learning and data mining algorithms, as this is a larger organism with more data available, and a more challenging functional classification. Results Novel extensions to the machine learning and data mining algorithms have been devised in order to deal with the challenges. Accurate rules have been learned and predictions have been made for many of the ORFs whose function is currently unknown. The rules are informative, agree with known biology and allow for scientific discovery. Availability All predictions are freely available from, all datasets used in this study are freely available from and software for relational data mining is available from en
dc.language.iso eng
dc.relation.ispartof Bioinformatics en
dc.rights en
dc.subject prediction en
dc.subject DMP en
dc.subject yeast en
dc.subject scientific discovery en
dc.subject S. cerevisiae en
dc.subject functional genomics en
dc.title Predicting gene function in Saccharomyces cerevisiae 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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