Predicting gene function in Saccharomyces cerevisiae

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dc.contributor.author King, Ross Donald
dc.contributor.author Clare, Amanda
dc.date.accessioned 2006-04-24T15:26:10Z
dc.date.available 2006-04-24T15:26:10Z
dc.date.issued 2003
dc.identifier.citation King , R D & Clare , A 2003 , ' Predicting gene function in Saccharomyces cerevisiae ' Bioinformatics , vol 19 , no. S2 , pp. 42-49 . en
dc.identifier.issn 1367-4803
dc.identifier.other PURE: 68068
dc.identifier.other dspace: 2160/129
dc.identifier.uri http://hdl.handle.net/2160/129
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 http://www.genepredictions.org, all datasets used in this study are freely available from http://www.aber.ac.uk/compsci/Research/bio/dss/yeastdata and software for relational data mining is available from http://www.aber.ac.uk/compsci/Research/bio/dss/polyfarm. en
dc.language.iso eng
dc.relation.ispartof Bioinformatics 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 Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1093/bioinformatics/btg1058
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Computational Biology and Bioinformatics en
dc.description.status Peer reviewed en


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