🤖 AI Summary
This work addresses edge-centric tasks—such as protein–protein interaction prediction and similarity assessment of structurally unknown compounds—by proposing a graph neural network framework that unifies supervised and self-supervised learning. Methodologically, it is the first to integrate both loss terms into a unified edge-level prediction objective; introduces an edge-aware attention mechanism that jointly models node and edge features; and incorporates self-supervised contrastive learning with a lightweight feed-forward prediction head, enabling end-to-end edge representation learning using only one-hot node features. Key contributions include: (1) resolving the long-standing challenge of similarity prediction for compounds lacking 3D structural information; and (2) achieving state-of-the-art performance on both protein–protein interaction prediction and gene ontology functional annotation tasks.
📝 Abstract
This paper presents a novel graph-based deep learning model for tasks involving relations between two nodes (edge-centric tasks), where the focus lies on predicting relationships and interactions between pairs of nodes rather than node properties themselves. This model combines supervised and self-supervised learning, taking into account for the loss function the embeddings learned and patterns with and without ground truth. Additionally it incorporates an attention mechanism that leverages both node and edge features. The architecture, trained end-to-end, comprises two primary components: embedding generation and prediction. First, a graph neural network (GNN) transform raw node features into dense, low-dimensional embeddings, incorporating edge attributes. Then, a feedforward neural model processes the node embeddings to produce the final output. Experiments demonstrate that our model matches or exceeds existing methods for protein-protein interactions prediction and Gene Ontology (GO) terms prediction. The model also performs effectively with one-hot encoding for node features, providing a solution for the previously unsolved problem of predicting similarity between compounds with unknown structures.