BMC Genomics Research Extending bicluster analysis to annotate unclassified ORFs and predict novel functional modules using expression data Kenneth Bryan* and Pádraig Cunningham Address: Complex and Adaptive Systems Laboratory (CASL), University College Dublin, Belfield, Dublin 4, Ireland Email: Kenneth Bryan* - kenneth.bryan@ucd.ie; Pádraig Cunningham - padraig.cunningham@ucd.ie * Corresponding author BioMed Central Open Access from IEEE 7th International Conference on Bioinformatics and Bioengineering at Harvard Medical School Boston, MA, USA. 14–17 October 2007 Published: 16 September 2008 BMC Genomics 2008, 9(Suppl 2):S20 doi:10.1186/1471-2164-9-S2-S20 <supplement><title><p>IEE 7<sup>th</sup>InternationalConferenceonBioinformaticsandBioengine ringatHarvardMedicalSchool</p></title><editor>MaryQuYang,JackYYang,HamidRArabniaandYoupingDeng</editor><note>Research</note><url>htp:/www.biomedcentral.com/content/pdf/1471-2164-9-S2-info.pdf</url></sup lement> This article is available from: http://www.biomedcentral.com/1471-2164/9/S2/S20 © 2008 Bryan and Cunningham; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: Microarrays have the capacity to measure the expressions of thousands of genes in parallel over many experimental samples. The unsupervised classification technique of bicluster analysis has been employed previously to uncover gene expression correlations over subsets of samples with the aim of providing a more accurate model of the natural gene functional classes. This approach also has the potential to aid functional annotation of unclassified open reading frames (ORFs). Until now this aspect of biclustering has been under-explored. In this work we illustrate how bicluster analysis may be extended into a 'semi-supervised' ORF annotation approach referred to as BALBOA. Results: The efficacy of the BALBOA ORF classification technique is first assessed via cross validation and compared to a multi-class k-Nearest Neighbour (kNN) benchmark across three independent gene expression datasets. BALBOA is then used to assign putative functional annotations to unclassified yeast ORFs. These predictions are evaluated using existing experimental and protein sequence information. Lastly, we employ a related semi-supervised method to predict the presence of novel functional modules within yeast. Conclusion: In this paper we demonstrate how unsupervised classification methods, such as bicluster analysis, may be extended using of available annotations to form semi-supervised approaches within the gene expression analysis domain. We show that such methods have the potential to improve upon supervised approaches and shed new light on the functions of unclassified ORFs and their co-regulation. Background Gene expression microarrays enable the expressions of thousands of genes to be measured in parallel over many experimental samples (growth conditions, time points, cell types etc.). The results from microarray experiments are generally presented in the form of an expression data matrix, where rows represent genes and columns repre- sent samples (or vice versa depending on the experimen- tal objective). Analysis of such gene expression data has shown that functionally related genes may have correlated Page 1 of 15 (page number not for citation purposes)