LPI-IBNRA: Long Non-coding RNA-Protein Interaction Prediction Based on Improved Bipartite Network Recommender Algorithm

Conclusion In this article, we proposed a novel method LPI-IBNRA for predicting lncRNA-protein interactions, based on the known lncRNA-protein interactions, lncRNA expression similarity and protein-protein interactions. We integrated the known interactions and similarity as the initial resource scores for a two-round resource allocation of a bipartite network recommendation. Furthermore, we optimized the weight matrix by eliminating second-order correlations appropriately, to obtain the final result of lncRNA-protein interaction prediction. We finally acquired gratifying and reliable prediction performance in LOOCV, 10-fold cross evaluation and case studies. Thus, we believe that LPI-IBNRA can make reliable predictions and might guide future experimental studies on lncRNA-protein interactions. LPI-IBNRA has the following improvements over several previous methods in predicting lncRNA-protein interactions. First, with the employment of the bipartite network recommender algorithm, we utilized the known lncRNA-protein interactions to construct a bipartite network between lncRNAs and proteins, and then allocated the resource scores via interaction edges between lncRNA nodes and protein nodes. Therefore, the negative sample set is not required in our methods. Second, we assigned weights to each edge on the bipartite network, which is distinct from most former bipartite network methods. Thus, the resource scores would not be evenly distributed during the resource allocation proce...
Source: Frontiers in Genetics - Category: Genetics & Stem Cells Source Type: research