It is generally expected that next-generation sequencing technology (NGS) will lead to more precise, faster, cheaper and overall more efficient and universal diagnostic methods for rare diseases. An essential step in the diagnosis of these diseases is the evaluation of genetic variants. This involves identifying the few variants that are relevant for medical diagnosis from the large number of genetic variants found. To support this process, variant prioritization methods are used. They use curated gene-phenotype or gene-disease associations to evaluate the relevance of the variants. However, these methods have significant limitations with respect to the explainability of the prioritization resuls and the use of additional data sources. Therefore, this project aims to develop a self-adaptive system that uses different medical and clinical data to incrementally improve the evaluation of gene variants. For this purpose, approaches from the area of the explainability of machine learning processes, workflows and provenance are evaluated, combined and, if necessary, adapted. In addition, methods from the field of modeling and simulation are used to develop and evaluate the system.