StellarPath: Hierarchical-vertical multi-omics classifier synergizes stable markers and interpretable similarity networks for patient profiling

The Patient Similarity Network paradigm implies modeling the similarity between patients based on specific data. The similarity can summarize patients’ relationships from high-dimensional data, such as biological omics. The end PSN can undergo un/supervised learning tasks while being strongly interpretable, tailored for precision medicine, and ready to be analyzed with graph-theory methods. However, these benefits are not guaranteed and depend on the granularity of the summarized data, the clarity of the similarity measure, the complexity of the network’s topology, and the implemented methods for analysis. To date, no patient classifier fully leverages the paradigm’s inherent benefits. PSNs remain complex, unexploited, and meaningless. We present StellarPath, a hierarchical-vertical patient classifier that leverages pathway analysis and patient similarity concepts to find meaningful features for both classes and individuals. StellarPath processes omics data, hierarchically integrates them into pathways, and uses a novel similarity to measure how patients’ pathway activity is alike. It selects biologically relevant molecules, pathways, and networks, considering molecule stability and topology. A graph convolutional neural network then predicts unknown patients based on known cases. StellarPath excels in classification performances and computational resources across sixteen datasets. It demonstrates proficiency in inferring the class of new patients described in external independent studies, following its initial training and testing phases on a local dataset. It advances the PSN paradigm and provides new markers, insights, and tools for in-depth patient profiling.Author summary: A clinician’s decision-making process for diagnosing an unknown patient involves selecting properties where patients with the same condition are alike but distinct from others, creating a mental database of known patients linked by their similarities, and evaluating the condition of the unknown patient based on its similarities with those in the database. However, constructing a network from biological omics data, which profiles patients through thousands of molecules, poses a significant challenge. The network’s quality hinges on the choice of the molecules used to determine the patients’ similarities, the significance of the similarity measure, and the network’s structure interpretability. StellarPath is a patient classifier based on patient similarity networks. It manages data normalization, molecule selection, and combines molecule types based on their interactions. It determines key cellular functions distinguishing patient classes and models how much two patients are similar based on how their cells regulate each function. It retains networks that group similar patients while separating different ones, using this data to predict unknown patients’ outcomes. Tested on seventeen datasets, StellarPath has proven efficient and insightful. It pushes the patient similarity network paradigm forward, offers reliable markers, and provides new tools and insights for patient analysis.