Comorbidity is vital for disease understanding and management. In graph machine learning, it is seen as a result of mutations in disease-associated genes linked through the protein-protein interactions (PPI) of the human interactome. The incomplete human interactome however presents challenges in extracting useful features for comorbidity prediction. In this study, we introduce a new method called Biologically Supervised Graph Embedding (BSE) to select the most relevant features from the graph embedding for disease subgraphs relation representation, improving the accuracy of predicting comorbid disease pairs. Our investigation into BSE’s impact on both centered and uncentered embedding methods showcases its consistent superiority over the state-of-the-art techniques and its adeptness in selecting features enriched with vital biological insights, thereby improving prediction performance significantly, up to 50% when measured by ROC AUC score. Further analysis indicates that BSE consistently and substantially improves the ratio of disease associations to gene connectivity, affirming its potential in uncovering latent biological factors affecting comorbidity. The study also reveals additional statistically significant enhancements across various metrics, further highlighting BSE’s potential to introduce novel avenues for precise disease comorbidity predictions and other potential applications. The GitHub repository containing the source code can be accessed at the following link: https://github.com/xihan-qin/Biologically-Supervised-Graph-Embedding .