Multi-Task Learning Framework for Cancer Driver Gene Identification on Multi-Network and Multi-OMICS Data

Cancer driver genes play an essential role in understanding cancer oncogenesis, tumor progression, and thera-peutic development. The integration of multi-omics data with biological networks has enabled the application of graph deep learning techniques for identifying cancer driver genes. However, most existing methods only use a single biological network as input, inevitably introducing the incompleteness and noise of interactions into models. To address these limitations, we propose MTCDG, a multi-task learning framework for cancer driver gene identification on multi-network and multi-omics data, which can not only enhance the interaction completeness but also enable more comprehensive extraction of graph topological features. The experimental results show the superior predictive performance of MTCDG over other methods. We anticipate that MTCDG will offer new insights for cancer genomic research and can be potentially extended to other areas of biological research in future research. The code of MTCDG is available on github: https://github.com/xingyili/MTCDG.