Figure 2From: Extending bicluster analysis to annotate unclassified ORFs and predict novel functional modules using expression dataIllustration of the steps in the BALBOA ORF classification algorithm. This figure shows the various step of the BALBOA ORF prediction algorithm. In step 1 the expression dataset is divided into its annotated genes and unannotated (unclassified) ORFs. In step 2 biclusters are generated in the annotated gene set only. In step 3 selected biclusters (where E ≥ E max ) are used to classify similarly expressed ORFs in the unclassified set. In step 4 ORFs are combined into weighted frequency list for each functional category. Each ORF label weight is derived from the functional enrichments of the classifying biclusters. In step 5 the top ORFs (where F ≥ F max ) are selected from this list. In step 6 ORFs consistently classified across independent datasets are returned.Back to article page