Replication Data for: Integrative Multi-Omics, Deep Learning and Network Biology in Precision Oncology

Multiple genetic and epigenetic alterations characterize tumor progression and define the hallmarks of cancer. Precision oncology has evolved as a form of cancer therapy that focuses on the genetic and molecular profiling of tumors to identify specific molecular changes for personalized cancer treatment. Advances in high-throughput sequencing technologies have provided a huge amount of multiomic sequencing data contributing
enormously to this development. Integrating and analyzing diverse multi-omics data using computational techniques, including artificial intelligence and deep learning, are crucial in this regard, as they can reveal critical molecular changes and alterations in signaling networks within tumors to help identify disease development patterns for better outcomes. Additionally, AI powered multi-omics and network biology have been effectively employed to decipher and exploit molecular networks for solving key problems facing precision oncology. This article aims to provide a scoping review of this area of cancer research in the context of cutting-edge developments in associated tools and techniques to better understand the scope and importance of precision oncology in realizing the intended goals.