selfRL: Two-Level Self-Supervised Transformer Representation Learning for Link Prediction of Heterogeneous Biomedical Networks

Abstract

Predicting potential links in heterogeneous biomedical networks (HBNs) can greatly benefit various important biomedical problem. However, the self-supervised representation learning for link prediction in HBNs has been slightly explored in previous researches. Therefore, this study proposes a two-level self-supervised representation learning, namely selfRL, for link prediction in heterogeneous biomedical networks. The meta path detection-based self-supervised learning task is proposed to learn representation vectors that can capture the global-level structure and semantic feature in HBNs. The vertex entity mask-based self-supervised learning mechanism is designed to enhance local association of vertices. Finally, the representations from two tasks are concatenated to generate high-quality representation vectors. The results of link prediction on six datasets show selfRL outperforms 25 state-of-the-art methods. In particular, selfRL reveals great performance with results close to 1 in terms of AUC and AUPR on the NeoDTI-net dataset. In addition, the PubMed publications demonstrate that nine out of ten drugs screened by selfRL can inhibit the cytokine storm in COVID-19 patients. In summary, selfRL provides a general frame-work that develops self-supervised learning tasks with unlabeled data to obtain promising representations for improving link prediction.