Predicting lncRNA-miRNA interactions based on logistic matrix factorization with neighborhood regularized

Abstract Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) interactions play important roles in diagnostic biomarkers and therapeutic targets for various human diseases. However, experimental methods for finding miRNAs associated with a particular lncRNA are costly, time consuming, and only a few theoretical approaches play a role in predicting potential lncRNA-miRNA associations. In this study, we have established a novel matrix factorization model to predict lncRNA-miRNA interactions, namely lncRNA-miRNA interactions prediction by logistic matrix factorization with neighborhood regularized (LMFNRLMI). Meanwhile, it only utilizes known positive samples to mine potential associations in data that lack negative samples. As a result, this new model obtaines reliable performance in the leave-one-out cross validation (the AUC of 0.9319) and 5-fold cross validation (the AUC of 0.9220), which has significantly improved performance in predicting potential lncRNA-miRNA associations compared to other models. Furthermore, comparison with several other network algorithms, and test based on all kinds of similarity, our model successfully confirms the superiority of LMFNRLMI. Whereby, we hope that LMFNRLMI can be a useful tool for potential lncRNA-miRNA association identification in the future.