BMC Genomics 2008, 9(Suppl 2):S20 http://www.biomedcentral.com/1471-2164/9/S2/S20 PFrigeduircete4d functional module supported by biclusters enriched with Transcription: Ribosomal RNA processing (11.04.01) Predicted functional module supported by biclusters enriched with Transcription: Ribosomal RNA processing (11.04.01). This figure shows the largest group of unclassified ORFS that were consistently classified together by biclusters significantly enriched for Transcription: Ribosomal RNA processing (11.04.01) in three independent datasets. The 19 unclassified ORFs in this predicted functional module correlate tightly over a subset of 30 sample features in the (a) Eisen, (b) Gasch and (c) Hughes expression datasets. achieves improved results over multi-class implementa- tions of majority kNN and the more competitive unani- mous kNN. Unlike kNN, BALBOA caters for 'local' correlations over feature subsets. This seems to allow for markedly improved classification precision for certain functional categories such as Transcription (11). This thus demonstrates the necessity for classification approaches in this domain to provide for the prospect of sub-space similarities. kNN is still a powerful technique when fea- tures are relevant and it may prove interesting in future work to perform kNN with those features selected by bicluster analysis. In the second part of the Results section, we saw that BALBOA's ORF predictions were well sup- ported by external functional evidence from the Saccharo- myces Genome Database (SGD). Strong support, in particular, was provided by the recent official MIPS anno- tation of YCR072C. Although these annotations are puta- tive they may still aid in the design of improved 'wet lab' experiments that may in turn lead to official annotation. In the last part of the Results section, we examined groups of unclassified ORFs consistently predicted together across two or more independent datasets with the aim of modelling functional modules present within the set of yeast unclassified ORFs. The most prominent signal was the large correlated set of 19 ORFs labelled as rRNA processing (11.04.01). Cross dataset analysis and addi- tional supporting functional evidence supports this grouping and predicted function and this may be a good candidate set for further 'wet-lab' analysis. It is common to combine data from individual expression studies into one large dataset prior to analysis. However, we have shown that maintaining multiple perspectives or data 'views' allows dataset cross referencing and poten- tially increases support for findings. Also, sample sets from different sources may reveal different ORF functions. Maintaining multiple perspectives may also prove useful in standard bicluster and cluster analyses. Lastly, this work demonstrates the benefit of applying newly developed data mining techniques to re-examine expression data after initial studies. In this paper we demonstrate the potential new insights, in this case into unclassified ORF PFrigeduircete5d functional module supported by biclusters enriched with DNA Topology (10.01.02) Predicted functional module supported by biclusters enriched with DNA Topology (10.01.02). This figure shows a group of unclassified ORFS that were consistently classified together by biclusters significantly enriched for the DNA Topology (10.01.02) in three independent datasets. The 4 unclassified ORFs in this predicted functional module correlate tightly over a subset of 30 sample features in the (a) Eisen, (b) Gasch and (c) Hughes expression datasets. Page 13 of 15 (page number not for citation purposes)