Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction
This addresses a critical bottleneck in computational biology for researchers studying disease mechanisms, though it is incremental as it builds on existing graph neural network approaches.
The paper tackles the problem of poor performance in predicting interactions between novel proteins in multi-type Protein-Protein Interaction (PPI) prediction, proposing a new evaluation framework and a graph neural network method (GNN-PPI) that significantly outperforms state-of-the-art methods, especially for inter-novel-protein interactions.
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. As a result, we propose to address the problem from both the evaluation and the methodology. Firstly, we design a new evaluation framework that fully respects the inter-novel-protein interactions and gives consistent assessment across datasets. Secondly, we argue that correlations between proteins must provide useful information for analysis of novel proteins, and based on this, we propose a graph neural network based method (GNN-PPI) for better inter-novel-protein interaction prediction. Experimental results on real-world datasets of different scales demonstrate that GNN-PPI significantly outperforms state-of-the-art PPI prediction methods, especially for the inter-novel-protein interaction prediction.