A graph neural network model for inferring interindividual variation from experimental biological data

Interindividual variation in biological responses to physiological stimuli is a widely recognized phenomenon. However, effective computational tools for identifying the individual-specific mechanisms remain limited. We present a graph neural network (GNN) model designed to infer hidden molecular and physiological relationships underlying such variation in experimental biological data. To ensure applicability at a laboratory scale, the model was trained on a domain-specific corpus constructed from approximately 65K published studies containing the keyword “skeletal muscle”. The architecture comprises five layers with a multi-head attention mechanism and a multi-layer perceptron, enabling the model to capture both local topological features and directional dominance between connected nodes. The GNN was trained to learn relationships from experimental models to target features, as well as among target features. Using real experimental input consisting of differential gene expression data from mouse skeletal muscle subjected to acute exercise, the model successfully inferred individualized networks, identifying both common and unique paths across individuals based on input experimental context. These results demonstrate the model’s capacity to extract interpretable, individual-specific biological connectivity patterns. The proposed framework serves as a proof of concept for customizable, context-based GNN inference designed to address biological variation at the individual level.