The percentage of known colorectal cancer (CRC) genes in top 50–500 MDMs inferred from German dataset.

<p>Known CRC genes were collected from the PubGene (A) or OMIM (B). The percentages were compared with those in top differentially expressed genes (t-test genes) with the same number of genes in top ranked N modules, or GO gene sets with the same amount of top ranked N modules.</p

The prognosis prediction performance.

<p>The comparison of AUC (A) and accuracy (B) for three datasets: Different coloring schemes and shape indicate three independent datasets (orange circle: German dataset; blue diamond: Barrier dataset; green square: GSE5206 dataset). TX_Y methods (X: top 500 or 1000 MDMs; Y: 10 or 18 reference tumors or Leave-One-Out method (LOO)). The filled symbols denote the mean of AUCs; The comparison of accuracies(C), sensitivities (D) and specificities (E) for prognosis prediction between our method and present methods with same datasets, including the LOO results from Lin07 (L) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033653#pone.0033653-Lin1" target="_blank">[3]</a>, Garman08 (G) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033653#pone.0033653-Garman2" target="_blank">[42]</a>, Barrier06 (B) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033653#pone.0033653-Barrier1" target="_blank">[5]</a>, and also the Barrier06's results obtained using 34 tumors (TS34), 18 tumors (TS18) or 10 tumors(TS 10) as the training set. The filled symbols are mean value. *The points in the dotted circle are the outcomes from the methods which were validated using makers discovered by the one and the same dataset.</p

Schematic overview of most differentially expressed modules identification.

<p>Identifying the most differentially expressed modules include three key steps. First, the GO co-expressed network is constructed by combined the protein-protein interaction network, which was from the HPRD and BioGRID database, and GO gene sets together. The edges of network were weighed by co-expression level between their corresponding linked nodes. Second, functional modules were identified by the weighted Girvan-Newman algorithm <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033653#pone.0033653-Newman1" target="_blank">[32]</a>. Finally, functional modules were ranked on their differential levels between recurrent and non-recurrent tumors which were evaluated by the p-SAGE algorithm <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033653#pone.0033653-Huang1" target="_blank">[38]</a>.</p

The percentage of overlapping genes in top 100–1000 modules identified from two independent datasets, German and Barrier.

<p>The overlapping percentage is calculated as the ratio for the number of intersection and union of the genes. We compared the percentage of overlapping genes on top ranked N modules, top t test genes with the same number of genes in top N modules, and their corresponding permutation test controls.</p