Show simple item record Clare, Amanda King, Ross Donald 2006-04-25T15:59:11Z 2006-04-25T15:59:11Z 2002
dc.identifier.citation Clare , A & King , R D 2002 , ' How well do we understand the clusters found in microarray data? ' In Silico Biology , vol 2 , no. 4 , pp. 511-522 . en
dc.identifier.issn 1434-3207
dc.identifier.other PURE: 68573
dc.identifier.other PURE UUID: 4e684f30-a022-4f37-b825-56fd34d81142
dc.identifier.other dspace: 2160/159
dc.identifier.other DSpace_20121128.csv: row: 129
dc.identifier.other Scopus: 0036940312
dc.identifier.uri en
dc.description Clare, A. and King R.D. (2002) How well do we understand the clusters found in microarray data? In In Silico Biol. 2, 0046 en
dc.description.abstract We wished to quantify the state-of-the-art of our understanding of clusters in microarray data. To do this we systematically compared the clusters produced on sets of microarray data using a representative set of clustering algorithms (hierarchical, k-means, and a modified version of QT_CLUST) with the annotation schemes MIPS, GeneOntology and GenProtEC. We assumed that if a cluster reflected known biology its members would share related ontological annotations. This assumption is the basis of 'guilt-by-association' and is commonly used to assign the putative function of proteins. To statistically measure the relationship between cluster and annotation we developed a new predictive discriminatory measure. We found that the clusters found in microarray data do not in general agree with functional annotation classes. Although many statistically significant relationships can be found, the majority of clusters are not related to known biology (as described in annotation ontologies). This implies that use of guilt-by-association is not supported by annotation ontologies. Depending on the estimate of the amount of noise in the data, our results suggest that bioinformatics has only codified a small proportion of the biological knowledge required to understand microarray data. The annotated clusters can be found at en
dc.language.iso eng
dc.relation.ispartof In Silico Biology en
dc.rights en
dc.title How well do we understand the clusters found in microarray 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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