A novel approach to identify subtype-specific network biomarkers of breast cancer survivability

Sheikh Jubair, Abedalrhman Alkhateeb, Ashraf Abou Tabl, Luis Rueda, Alioune Ngom

Abstract: Increasing the survival rates for breast cancer has gained significant researcher interest. However, current studies reveal that a small subset of gene makers can predict survivability for people with different breast cancer subtypes. In these studies, the selected genes are not necessarily functionally related, and hence, they may not correctly indicate the molecular mechanism behind breast cancer survivability. Also, several studies have shown there is a very low overlap between the biomarkers subsets for the same cancer disease. To improve the robustness of the classification performance and stability of detected biomarkers, recent methods involve taking existing knowledge on relations between genes into account in the classifier by aggregating functionality-related genes to produce discriminative gene subnetworks called network biomarkers.