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

ABSTRACT: 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 the bioreaction-variation network, 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 65 K 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

SUBMITTER: Kawano F 

PROVIDER: S-EPMC12612280 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature