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Currently, there are some paradigm shifts in medicine, from the search for a single ideal biomarker, to the search for panels of molecules, and from a reductionistic to a systemic view, placing these molecules on functional networks. There is also a general trend to favor non-invasive biomarkers. Identifying non-invasive biomarkers in high-throughput data, having thousands of features and only tens of samples is not trivial. Here, we proposed a methodology and the related concepts to develop intelligent molecular biomarkers, via knowledge mining and knowledge discovery in data, illustrated on prostate cancer diagnosis. An informed feature selection is done by mining knowledge about pathways involved in prostate cancer, in specialized data bases. A knowledge discovery in data approach, with soft computing methods, is used to identify the relevant features and discover their relationships with clinical outcomes. The intelligent non-invasive diagnosis systems, is based on a team of mathematical models, discovered with genetic programming, and taking as inputs eight serum angiogenic molecules and PSA. This systems share with other intelligent systems we build, using this methodology but different soft computing techniques, and in different clinical settings - chronic hepatitis, bladder cancer, and prostate cancer - the best published accuracy, even 100%. Soft computing could be a strong foundation for the newly emerging Knowledge-Based-Medicine. The impact on medical practice could be enormous. Instead of offering just hints to the clinicians, like Evidence-Based-Medicine, Knowledge-Based-Medicine which is made possible and co-exists with Evidence-Based-Medicine, offers intelligent clinical decision supports systems.