We implemented Gaussian Mixture Model (GMM) to support personalized medicine through network science. By analyzing gene presence data from sick and healthy patients, the goal is to uncover hidden correlations between genes that may indicate disease. Our model assumes healthy patients exhibit uncorrelated gene distributions, while sick patients show strong correlations. A GMM is applied to distinguish these patterns, with prior knowledge scaling to improve accuracy. Performance is evaluated using KL-divergence and statistical validation with p-values, confirming that the GMM outperforms random guessing.