LPLSG: Prediction of lncRNA-protein Interaction Based on Local Network Structure

Abstract

Background: The interaction between RNA and protein plays an important role in life activities. Long ncRNAs (lncRNAs) are large non-coding RNAs, and have received extensive attention in recent years. Because the interaction between RNA and protein is tissue-specific and condition-specific, it is time-consuming and expensive to predict the interaction between lncRNA and protein based on biological wet experiments. Objective: The contribution of this paper is to propose a method for prediction based on the local structural similarity of lncRNA-protein interaction (LPI) network. Methods: The method computes the local structure similarity of network space, and maps it to LPI space, and uses an innovative algorithm that combined Resource Allocation and improved Collaborative Filtering algorithm to calculate the potential LPI. Conclusion: AUPR and AUC are significantly better than the five popular baseline methods. In addition, the case study shows that some results of LPLSG prediction on the actual data set have been verified by NPInterV4.0 database and some literatures.