A Novel Computational Method for Predicting LncRNA-Disease Associations from Heterogeneous Information Network with SDNE Embedding Model

Recent studies have shown that lncRNAs play a critical role in numerous complex human diseases. Thus, identification of lncRNA and diseases associations can help us to understand disease pathogenesis at the molecular level and develop disease diagnostic biomarkers. In this paper, a novel computational method LDAMAN is proposed to predict potential lncRNA-disease interactions from heterogeneous information network with SDNE embedding model. Specifically, known associations among lncRNA, disease, microRNA, circular RNA, mRNA, protein, drug and microbe are integrated to construct a molecular association network and a network embedding model SDNE is employed to extract network behavior features of lncRNA and disease nodes. Finally, the XGBoost classifier is used for predicting potential lncRNA-disease associations. In the experiment, the proposed method obtained stable AUC of 92.58% using 5-fold cross validation. In summary, the experimental results demonstrate our method provides a systematic landscape and computational prediction tool for lncRNA-disease association prediction.