All Competitors

ECG foundation model that reads any subset of the 12 standard leads natively, encoding recordings as variable-size spatiotemporal graphs.

Graph attention model that learns context-aware protein embeddings from protein-protein interaction, co-expression, and tissue association networks.

Graph-attention model that predicts A-to-I RNA editing from sequence and secondary structure, treating RNA as a graph with base-pairing edges.

Pfam domain annotation with a graph neural network over AlphaFold2 residue-interaction graphs and ESM-2 embeddings, one model for all families.

Protein-protein interface prediction from 3D structure using face-centered surface fingerprints and geometric graph attention, at ROC AUC 0.89.

Spatial proteomics imputation model inferring surface protein abundance from transcriptomics-only tissue sections via dual graph attention networks.

Graph attention foundation model for spatial transcriptomics that assigns spatial domains zero-shot across gene panels, tissues, and technologies.