DHOSGR: lncRNA-disease Association Prediction Based on Decay High-order Similarity and Graph-regularized Matrix Completion

Background: It has been shown in numerous recent studies that long non-coding RNAs (lncRNAs) play a vital role in the regulation of various biological processes, as well as serve as a basis for understanding the causes of human illnesses. Thus, many researchers have developed matrix completion approaches to infer lncRNA-disease connections and enhance prediction performance by using similarity information. Objective Most matrix completion approaches are solely based on the first-order or second-order similarity between nodes, and higher-order similarity is rarely considered. In view of this, we developed a computational method to incorporate higher-order similarity information into the similarity network with different weights using a decay function designed by a random walk with restart (DHOSGR). Methods First, considering that the information will decay as the distance increases during network propagation, we defined a novel decay high-order similarity by combining the similarity matrix and its high-order similarity information through a decay function to construct a similarity network. Then, we applied the similarity network to the objective function as a graph regularization term. Finally, a proximal splitting algorithm was used to perform matrix completion to infer relationships between diseases and lncRNAs. Results In the experiment, DHOSGR achieves a superior performance in leave-one-out cross validation (LOOCV) and 100 times 5-fold cross validation (5-fold-CV), with AUC values of 0.9459 and 0.9334 +/- 0.0016, respectively, which are better than other five previous models. Moreover, case studies of three diseases (leukemia, lymphoma, and squamous cell carcinoma) demonstrated that DHOSGR can reliably predict associated lncRNAs. Conclusion DHOSGR can serve as a high efficiency calculation model for predicting lncRNA-disease associations.