BMC Genomics 2008, 9(Suppl 2):S20 http://www.biomedcentral.com/1471-2164/9/S2/S20 Table 1: Comparative cross validation of BALBOA with majority & unanimous voting kNN. KNN UkNN BALBOA bicluster classifiers to predict the functional labels of the test set. We perform two rounds of 4-fold cross validation. This 3:1 split reflects the natural annotated to unanno- tated ratio in yeast. In practice our a and β thresholds, described in the Methods section, are set as follows. Our set of best biclusters for each functional class are chosen by selecting biclusters with functional enrichment E ≥ αEmax, where α = 0.9 and Emax is the maximal enrichment for a functional class. We combine the resultant labelled ORFs for each class into a weighted frequency list and select the best supported ORFs, such that F ≥ βFmax, where β = 0.9 and Fmax is the label with the highest weighted fre- quency. Although we could finely tune these parameters to achieve better results for each functional class these val- ues allowed for best average predictions across all func- tional categories and all datasets. This fact may also reduce the possibility of over-fitting our training data. Cross vali- dations for each yeast dataset are given in Table 1. Here we compare BALBOA's classification precision (P) and recall (R) with majority and unanimous voting multi-class kNN. In column one we see that majority kNN has a high recall but low precision and as a result its use as an ORF classi- fier is unfeasible. In column two we can see that unani- mous voting markedly improves precision at the expense of recall. The final column shows that BALBOA out-per- forms unanimous kNN achieving a higher mean precision over all functional classes. As opposed to kNN, BALBOA allows for the capturing of signals that occur over subsets of the experimental fea- tures in the dataset. In the Transcription (11) functional category in particular this facet seems to allow for a greater classification precision to be achieved across all three datasets. In other categories such as Protein Synthesis (12) UkNN is more competitive. This is not surprising as it is well known that genes in this fundamental functional cat- egory are strongly co-expressed over most conditions. Functional annotation of unclassified ORFs Now that we have determined BALBOA's classification precision for each functional class we can apply it to clas- sify unannotated yeast ORFs. Heretofore our evaluation has focused on the top level MIPS categories (01, 02 etc.). However in some cases it may be possible to assign a more specific and informative functional label. A prime exam- ple is that of Protein Synthesis (12). Expression correlations of genes from this category are most often due to the highly co-regulated sub-category of ribosomal proteins (12.01.01). In this study we have also observed that this ribosomal co-regulation is also evident in the ribosomal RNA (rRNA) processing genes. This sub-category, specifi- cally rRNA processing (11.04.01), often dominates the biclusters that are functionally enriched for Transcription (11). These sub-category labels are provided where avail- able and allow for a more specific putative annotation. MIPS Category Eisen Metabolism (01) Energy (02) Cell Cycle (10) Transcription (11) Protein Synthesis (12) Protein Fate (14) Transp. Elements (38) Cell Fate (43) Mean Hughes Metabolism (01) Energy (02) Cell Cycle (10) Transcription (11) Protein Synthesis (12) Protein Fate (14) Prot. Bind. Func. (16) Cell Transport (20) Transp. Elements (38) Biogen. Cell. Comp. (42) Mean Gasch Metabolism (01) Energy (02) Cell Cycle (10) Transcription (11) Protein Synthesis (12) Protein Fate (14) Cell Transport (20) Transp. Elements (38) Biogen. Cell. Comp. (42) Mean P 0.46 0.48 0.48 0.47 0.51 0.38 0.29 0.30 P 0.47 0.59 0.49 0.44 0.52 0.39 0.29 0.36 0.38 0.32 P 0.46 0.52 0.46 0.45 0.50 0.40 0.36 0.79 0.33 R 0.43 0.15 0.41 0.36 0.52 0.26 0.57 0.09 R 0.50 0.16 0.24 0.43 0.47 0.24 0.14 0.20 0.48 0.15 R 0.49 0.20 0.29 0.37 0.55 0.29 0.20 0.52 0.12 0.48 0.34 0.64 0.06 0.77 0.10 The highest precisons for each MIPS category in each dataset evaluated are shown in bold. BALBOA achvieves the highest mean precision over all MIPS categories in each dataset. P = Pecision, R = Recall. Of the 954 unclassified ORFs, BALBOA made putative functional predictions for 135 from the Eisen expression data, 113 from the Gasch data and 119 from the Hughes data. These span 13 of the 17 MIPS functional categories. Interestingly, despite the different experimental condi- tions investigated in the three independent expression studies, 21 annotations, spanning 7 MIPS functional cate- Page 8 of 15 (page number not for citation purposes) 0.42 0.35 0.53 0.05 0.70 0.09 0.43 0.31 0.60 0.03 0.70 0.09 P 0.50 0.63 0.86 0.13 0.97 0.94 0.25 0.00 P 0.85 0.98 0.85 0.62 0.95 0.50 0.00 0.80 0.00 0.41 P 0.88 0.94 0.75 0.64 0.95 1.00 0.25 0.00 0.38 R 0.00 0.01 0.03 0.00 0.24 0.01 0.09 0.00 R 0.03 0.03 0.01 0.01 0.18 0.00 0.00 0.02 0.00 0.01 R 0.01 0.05 0.00 0.00 0.31 0.03 0.00 0.15 0.00 P 0.54 0.57 0.89 0.58 1.00 0.91 0.50 0.58 P 0.85 0.76 0.57 0.81 1.00 0.47 0.57 0.50 0.77 0.74 P 0.83 0.83 0.58 0.74 0.98 0.93 0.66 0.75 0.60 R 0.01 0.05 0.02 0.03 0.08 0.02 0.43 0.05 R 0.04 0.08 0.03 0.03 0.05 0.02 0.01 0.03 0.47 0.04 R 0.03 0.09 0.03 0.06 0.17 0.01 0.01 0.50 0.03