Advancements in Life Sciences (Biology, Biomedicine, Bioengineering, Ecology, etc.) are largely driven by the development of powerful technologies and computational tools. Many experimental results have confirmed the hypothesis that biological processes and disease evolution may be explained through the use of complex network models. However, the solution of world-real problems that are often modelled through huge volumes of data requires a multidisciplinary approach and poses new challenges to the field of High Performance Computing (HPC) at different levels:
• modeling and simulation of complex phenomena (human organ functions, evolution of diseases, sustainable energy systems , etc.);
• analysis of complex data structures (matrices and tensors) generated from experimental data;
• Graph Representation Learning through (but not limited to) HPC ;
• processing and analysis of massive amounts of data produced by modern technologies (omics and genome sequencing, functional, and anatomical imaging, High-Content Screening, etc.);
• extracting, merging, and understanding of information from different sources (merging different types of images, bridging imaging and omics features, etc.);