The applications of the uniConSig algorithm in the discovery of new gene functions. (A) Quantifying the role of human genes underlying different cancer entities based on the lists of known cancer genes for different cancers. Gene sets for different cancer types were collected from CGC. The resulting cancer gene ESs based on K-S tests for different cancer entities are shown at the top (gray columns). We calculated the cancer type-specific uniConSig scores for the human genome using these 12 cancer gene sets as training gene lists and then sorted all human genes by the median of their uniConSig scores (dashed blue line). The top five uniConSig scores for each gene are shown in the chart, which represent five different cancer types (different colored dots). Circles outlined in black indicate that the gene is included in the CGC database for that specific cancer type. Known cancer gene names are highlighted in red. The running sum of the random walk K-S test of CGC cancer gene set is shown to the left. Red lines are the genes that are on the CGC known cancer gene list. (B) uniConSig scores for Oncogenes and tumor suppressors. In this dot plot, the uniConSig scores calculated based on the oncogene gene set and tumor suppressor gene set from CGC are shown in y- and x-axis, respectively. The dashed line indicates the distinction line (D-line), which was calculated based on the ROC-like curve shown in Supplementary Figure S7, where we selected the D-line’s slope based on the maximum of the Youden index. Here, we define the distance of a gene to the D-line as ‘dConSig’ score. (C) Quantifying the functional relevance of human genes underlying diabetes or mismatch repair pathway based on the OMIM diabetes gene set or the KEGG mismatch repair gene set. uniConSig scores of each human gene were calculated based on OMIM diabetes gene set (left) or KEGG’s mismatch repair pathway gene set (right). Top 50 genes are shown in the plots. Red bars are the genes that are on the OMIM diabetes gene set (left) or KEGG mismatch repair pathway gene set (right).