Background#R##N#Effective cancer clinical outcome prediction for understanding of the mechanism of various types of cancer has been pursued using molecular-based data such as gene expression profiles, an approach that has promise for providing better diagnostics and supporting further therapies. However, clinical outcome prediction based on gene expression profiles varies between independent data sets. Further, single-gene expression outcome prediction is limited for cancer evaluation since genes do not act in isolation, but rather interact with other genes in complex signaling or regulatory networks. In addition, since pathways are more likely to co-operate together, it would be desirable to incorporate expert knowledge to combine pathways in a useful and informative manner.
['Do Kyoon Kim', 'Ruowang Li', 'Scott M. Dudek', 'Alex T. Frase', 'Sarah A. Pendergrass', 'Marylyn D. Ritchie']
Knowledge-driven genomic interactions: an application in ovarian cancer