Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction

Understand

The study of multi-type Protein-Protein Interaction (PPI) is fundamental for understanding biological processes from a systematic perspective and revealing disease mechanisms.

• Existing methods suffer from significant performance degradation when tested in unseen dataset.
• In this paper, we investigate the problem and find that it is mainly attributed to the poor performance for inter-novel-protein interaction prediction.
• However, current evaluations overlook the inter-novel-protein interactions, and thus fail to give an instructive assessment.