Abstract
With the rapid development of biological research, microRNAs (miRNA) have become an attractive topic because lots of experimental studies have revealed the significant associations between miRNAs and diseases. However, considering that experiments are expensive and time-consuming, computational methods for predicting associations between miRNAs and diseases have become increasingly crucial. In this study, we proposed a neighborhood regularized logistic matrix factorization method for miRNA-disease association prediction (NRLMFMDA) by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimentally validation of disease-miRNA association. We used Gaussian interaction profile kernel similarity to cover the shortage of the traditional similarity to make it more reasonable and complete. Furthermore, NRLMFMDA also considered the important influences of the neighborhood information and took full advantage of them to improve the accuracy of the miRNA-disease association prediction. We also improved the accuracy by giving higher weights to the known association data in the process of calculating the potential association probabilities. In the global and the local leave-one-out cross validation, NRLMFMDA got the AUCs of 0.9068 and 0.8239, respectively. Moreover, the average AUC of NRLMFMDA in 5-fold cross validation was 0.8976 ± 0.0034. All the three kinds of cross validations have shown significant advantages to a number of previous models. In the case studies of breast neoplasms, esophageal neoplasms and lymphoma according to known miRNA-disease associations in the recent version of HMDD database, there were 78, 80, and 74% of top 50 predicted related miRNAs verified to have associations with these three diseases, respectively. In the further case studies for new disease without any known related miRNAs and the previous version of HMDD database, there were also high proportions of the predicted miRNAs verified by experimental reports. All the validation experiment results have demonstrated the effectiveness and practicability of NRLFMDA to predict the potential miRNA-disease associations.
Highlights
MicroRNAs are a category of endogenous and short non-coding single-stranded RNAs (21∼24 nucleotides) which could regulate the gene expression by targeting mRNAs for cleavage or translational repression at the posttranscriptional level (Ambros, 2001, 2004; Bartel, 2004; Meister and Tuschl, 2004)
Leave-one-out cross validation (LOOCV) and 5-fold cross validation were applied to evaluate the performance of NRLMFMDA
After calculating prediction association scores of all the miRNA-disease pairs by NRLMFMDA, we compared the score of each test sample with all the candidate ones to observe whether its rank was above the threshold which was given in advance
Summary
MicroRNAs (miRNAs) are a category of endogenous and short non-coding single-stranded RNAs (21∼24 nucleotides) which could regulate the gene expression by targeting mRNAs for cleavage or translational repression at the posttranscriptional level (Ambros, 2001, 2004; Bartel, 2004; Meister and Tuschl, 2004). More and more experiments have been implemented to show that miRNAs have great connections with the various development processes of many human complex diseases (Lynam-Lennon et al, 2009; Meola et al, 2009; Huang et al, 2016b). There is an urgent need for us to make further efforts to develop novel computational models for potential miRNAs-disease association prediction. Many computational methods are well behaved in predicting miRNA-disease associations (Chen and Yan, 2013; Chen, 2015b; Chen et al, 2016a,g; Chen et al, 2018c). Further experimental studies can be more efficiently implemented by selecting the most promising associated miRNAs predicted by computational models
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