Medicine and healthcare are currently faced with a significant rise in their complexity. This is partly due to the progress made during the past three decades in the fundamental biological understanding of the causes of health and disease at the molecular, (sub)cellular, and organ level. It is also partly caused by increased specialisation of both biomedical research and clinical practice, and greater involvement of policy makers in healthcare to control the costs. Promises made by biomedical researchers that their research results will have clinical impact, e.g. that cancer can be cured by immune therapy, have also increased expectations from society about what healthcare is able to deliver. However, it is rarely the case that a discovery at the molecular level has immediate consequences for the diagnosis and treatment of patients. A major problem is that the progress made by the basic sciences increases the quantity of information that one has to deal with when making decisions at the level of the patient or healthcare in general. An additional problem is that this information arises from research at different levels: from the molecular level, via the cellular level, at one end of the spectrum, to the patient level at the other end. How to bridge these different levels is currently unclear although it has given rise to the creation of yet another field: translational medicine. However, even though there are huge differences in the techniques and methods used by biomedical researchers, there is now an increasing tendency to share research results in terms of formal knowledge representation methods, such as ontologies, statistical models, network models, and mathematical models. To support health-care professionals making the best possible decision, computer-based support based on such knowledge is now becoming increasingly important. It may also be the only way to integrate research results from the different parts of the spectrum of biomedical and clinical research.