Abstract Computational oncogenomics has a pivotal role to solve biological and
clinical issues and support translational medicine in cancer research through computer science and bioinformatics methods. Leveraging advanced computational
methods for comprehensive omics analysis is increasingly essential to deepen the
understanding of tumor molecular complexity. The research activity outlined in this
chapter emphasised the synergistic use of Data Science techniques and omics data
processing to tackle clinical challenges of cancer diseases and face their inherent
intricacy and heterogeneity. These often pose an insurmountable barrier to traditional research approaches; therefore, we designed and developed computational
workflows that follow every step of a typical Data Science process while being
enhanced and tailored for omics data, considering all their peculiarities and issues.
Overall, this research delivered innovative computational frameworks contributing
to unravelling cancer complexity, advancing personalized oncology, and paving the
way for precision medicine through comprehensive, clinically driven omics analysis.