Figure 1

The schema for the identification of novel genes associated with complex diseases. The expression profiles from the cancer data are analyzed to predict differentially expressed and condition-specific genes. The functional attributes over-represented in cancer are selected and representative datasets from public resources mined. The common cancer fingerprints from cancer-associated genes are processed through Boolean logic to develop a guilt-by-association classifier which, applied to non-cancer-associated genes, predicts novel candidate cancer-associated genes. Finally, novel candidate genes are further analyzed using network theory approaches.