Abstract
Nowadays, researchers have realized that microRNAs (miRNAs) are playing a significant role in many important biological processes and they are closely connected with various complex human diseases. However, since there are too many possible miRNA-disease associations to analyze, it remains difficult to predict the potential miRNAs related to human diseases without a systematic and effective method. In this study, we developed a Matrix Completion for MiRNA-Disease Association prediction model (MCMDA) based on the known miRNA-disease associations in HMDD database. MCMDA model utilized the matrix completion algorithm to update the adjacency matrix of known miRNA-disease associations and furthermore predict the potential associations. To evaluate the performance of MCMDA, we performed leave-one-out cross validation (LOOCV) and 5-fold cross validation to compare MCMDA with three previous classical computational models (RLSMDA, HDMP, and WBSMDA). As a result, MCMDA achieved AUCs of 0.8749 in global LOOCV, 0.7718 in local LOOCV and average AUC of 0.8767+/−0.0011 in 5-fold cross validation. Moreover, the prediction results associated with colon neoplasms, kidney neoplasms, lymphoma and prostate neoplasms were verified. As a consequence, 84%, 86%, 78% and 90% of the top 50 potential miRNAs for these four diseases were respectively confirmed by recent experimental discoveries. Therefore, MCMDA model is superior to the previous models in that it improves the prediction performance although it only depends on the known miRNA-disease associations.
Highlights
MicroRNA is a kind of short noncoding single-stranded RNA (~22nt) which can regulate the gene expression by binding to the 3’ untranslated regions (UTRs) of its target messenger RNA through base pairing [1, 2]
Researchers propose several computational methods to predict the potential associations between miRNAs and diseases because computational models could select the most promising miRNAs related to human diseases and are less expensive than the traditional experimental methods
In order to predict potential miRNA-disease associations, we developed a computational model of MCMDA by analyzing the known miRNA-disease associations and implementing the matrix completion algorithm to get the association score of each miRNA-disease pair
Summary
MicroRNA (miRNA) is a kind of short noncoding single-stranded RNA (~22nt) which can regulate the gene expression by binding to the 3’ untranslated regions (UTRs) of its target messenger RNA (mRNA) through base pairing [1, 2]. Based on plenty of biological experiments, researchers believe that these small molecules have a wide range of regulation effects on eukaryotic gene expression, in human genes and in genes of many other species [4]. Considering that large numbers of miRNA-associated datasets are available, computational methods are efficient in predicting miRNAdisease associations in that they can select the most promising associated miRNAs for further experimental studies [15,16,17]. It is necessary for us to make further efforts and develop efficient computational models to predict the potential miRNA-disease associations [16, 18,19,20,21,22,23,24,25,26,27,28,29,30,31]
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