Fig. 1: UniPMT framework. | Nature Machine Intelligence

Fig. 1: UniPMT framework.

From: A unified deep framework for peptide–major histocompatibility complex–T cell receptor binding prediction

Fig. 1

The P–M–T, P–M and P–T relationships are first represented as a graph, where the initial embedding of P and T is learned via ESM19, and that of M is its pseudo-sequence3. Then, a GNN is applied to learn the embeddings of each input node. Finally, a DMF-based learning strategy is applied to unify the binding prediction tasks for P–M–T, P–M and P–T. w and y denote the weights and prediction scores, respectively.

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