Prediction of oncogenic interactions and cancer-related signaling networks based on network topology.

ABSTRACT: Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predi

SUBMITTER: Acencio ML 

PROVIDER: S-EPMC3808429 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature