Aim and scope. Machine learning has become a pivotal tool for analyzing biomedical and biological datasets, especially in the era of Big Data. Machine learning algorithms can uncover hidden relationships and structures within healthcare data and leverage them to make accurate predictions about similar or future data instances. For example, machine learning software has successfully predicted cancer diagnoses by analyzing patients' clinical features, enabling scientists to save time and resources compared to traditional wet-lab experiments. Computational researchers have also applied machine learning to infer knowledge about patients by analyzing biological datasets, particularly those involving genetic and epigenomic traits. Data mining approaches applied to such datasets can lead to significant discoveries, deepening our understanding of molecular biology and providing new insights into patients’ diseases.

Moreover, recent advancements in this field anticipate the integration of clinical knowledge (represented in various forms such as medical protocols) into machine learning algorithms. By defining methods to incorporate and inject such knowledge, it becomes possible to harness the strengths of both knowledge-based and data-driven approaches.