A model for predicting ncRNA-protein interactions based on graph neural networks and community detection

Non-coding RNA (ncRNA) s play an considerable role in the current biological sciences, such as gene tran-scription, gene expression, etc. Exploring the ncRNA-protein interactions(NPI) is of great significance, while some experimental techniques are very expensive in terms of time consumption and labor cost. This has pro-moted the birth of some computational algorithms related to traditional statistics and artificial intelligence. However, these algorithms usually require the sequence or structural feature vector of the molecule. Although graph neural network (GNN) s has been widely used in recent academic and industrial researches, its potential remains unexplored in the field of detecting NPI. Hence, we present a novel GNN-based model to detect NPI in this paper, where the detecting problem of NPI is transformed into the graph link prediction problem. Specif-ically, the proposed method utilizes two groups of labels to distinguish two different types of nodes: ncRNA and protein, which alleviates the problem of over-coupling in graph network. Subsequently, ncRNA and protein embedding is initially optimized based on the cluster ownership relationship of nodes in the graph. Moreover, the model applies a self-attention mechanism to preserve the graph topology to reduce information loss during pooling. The experimental results indicate that the proposed model indeed has superior performance.