ArticlebioRxiv : the preprint server for biology2026
Graph neural network modeling of receptor interaction kinetics from single-molecule imaging data.
Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Single-molecule (SM) imaging (SMI)-based approaches have the powerful ability to capture receptor interactions - necessary for cell signaling - in their native live-cell environment. Yet, due to substoichiometric labeling, SMI generally provides only partial information on these interactions. We developed Deep-FISIK, which utilizes graph neural networks and multi-head attention for message-passing, to predict from SMI data the kinetics of homotypic interactions of the full receptor system. The input to Deep-FISIK are the SM detections in SMI experiments, without the need for explicit tracking. Thus, Deep-FISIK is compatible with labeling a higher fraction of receptors in the SMI experiments, increasing the prediction accuracy of the interaction kinetics parameters. Deep-FISIK's performance is robust in the presence of a variety of deviations from the training data, indicating Deep-FISIK's applicability to many receptor systems and SMI experiments.
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