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The machinery of life operates on the complex interactions between genes and proteins. Attempts to capture these interactions have culminated into the study of Genetic Networks. Genetic defects lead to erroneous interactions, which in turn lead to diseases. For personalized treatment of these diseases, a careful analysis of Genetic Networks and a patient’s genetic data is required. In this work, we co-design a novel probabilistic AI model along with a reconfigurable architecture to enable personalized treatment for cancer patients. This approach enables a cost-effective and scalable solution for widespread use of personalized medicine. Our model offers interpretability and realistic confidences in its predictions, which is essential for medical applications. The resulting personalized inference on a dataset of 3k patients agrees with doctor’s treatment choices in 80% of the cases. The other cases are diverging from the universal guideline, enabling individualized treatment options based on genetic data. Our architecture is validated on a hybrid SoC-FPGA platform which performs 25x faster than software, implemented on a 16-core Xeon workstation, while consuming 25x less power.