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The study of multi-type Protein-Protein Interaction (PPI) is fundamental forunderstanding biological processes from a systematic perspective and revealingdisease mechanisms. Existing methods suffer from significant performancedegradation when tested in unseen dataset. In this paper, we investigate theproblem and find that it is mainly attributed to the poor performance forinter-novel-protein interaction prediction. However, current evaluationsoverlook the inter-novel-protein interactions, and thus fail to give aninstructive assessment. As a result, we propose to address the problem fromboth the evaluation and the methodology. Firstly, we design a new evaluationframework that fully respects the inter-novel-protein interactions and givesconsistent assessment across datasets. Secondly, we argue that correlationsbetween proteins must provide useful information for analysis of novelproteins, and based on this, we propose a graph neural network based method(GNN-PPI) for better inter-novel-protein interaction prediction. Experimentalresults on real-world datasets of different scales demonstrate that GNN-PPIsignificantly outperforms state-of-the-art PPI prediction methods, especiallyfor the inter-novel-protein interaction prediction.

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