Identifying and ranking potential cancer drivers using representation learning on attributed network.

Cancer can arise as a consequence of the accumulation of genomic alterations. Only a small part of driver mutations contributes to cancer development and progression. Hence, the identification of genes and alterations that serve as drivers for cancer development plays a critical role in drug design, cancer diagnoses and treatment. In this study, we propose a novel method to identify potential cancer drivers by using a Representation Learning method on Attributed Graphs (called RLAG). It is a first attempt to use both network structure and node attributes to learn feature representation for the genes in the network. Then it leverages these feature vectors to divide the genes into several subgroups. Finally, potential cancer driver genes are prioritized according to ranking scores that measure both genes' properties and their importance in the subgroups. We apply our method to predict driver genes for lung cancer, breast cancer and prostate cancer. The results show that our method outperforms the other three state-of-the-art methods in terms of Precision, Recall and F1-score values.

Pubmed ID: 32758683