Abstract
miRNAs belong to small non-coding RNAs that are related to a number of complicated biological processes. Considerable studies have suggested that miRNAs are closely associated with many human diseases. In this study, we proposed a computational model based on Similarity Constrained Matrix Factorization for miRNA-Disease Association Prediction (SCMFMDA). In order to effectively combine different disease and miRNA similarity data, we applied similarity network fusion algorithm to obtain integrated disease similarity (composed of disease functional similarity, disease semantic similarity and disease Gaussian interaction profile kernel similarity) and integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity and miRNA Gaussian interaction profile kernel similarity). In addition, the L2 regularization terms and similarity constraint terms were added to traditional Nonnegative Matrix Factorization algorithm to predict disease-related miRNAs. SCMFMDA achieved AUCs of 0.9675 and 0.9447 based on global Leave-one-out cross validation and five-fold cross validation, respectively. Furthermore, the case studies on two common human diseases were also implemented to demonstrate the prediction accuracy of SCMFMDA. The out of top 50 predicted miRNAs confirmed by experimental reports that indicated SCMFMDA was effective for prediction of relationship between miRNAs and diseases.
Highlights
MicroRNAs are a number of 17-24nt non-coding RNAs, which act a pivotal part in controlling the expression of gene through RNA cleavage or translation repression [1,2,3]
Because miRNAs regulated the expression of a great quantity of target genes, the total miRNA pathway played a key role in gene expression control [7,8,9]. miRNAs are bound up with several crucial biological processes, such as cell development, cell differentiation, cell proliferation and so on [10]
Because there were several limitations in previous models, we presented a novel model based on Similarity Constrained Matrix Factorization for miRNA-Disease Association Prediction (SCMFMDA)
Summary
MicroRNAs (miRNAs) are a number of 17-24nt non-coding RNAs, which act a pivotal part in controlling the expression of gene through RNA cleavage or translation repression [1,2,3]. A large amount of miRNAs was discovered by researchers in experiments [4,5]. Developmental defects can be the result of the dysregulation of miRNAs that associate with progression of diseases [11]. Considerable studies have indicated that miRNAs are connected with a serious of human neoplasms, which include lung neoplasms [12], prostate neoplasms [13] and so on. Distinguishing miRNAs associated with diseases can deepen understanding of the genetic causes of complex diseases. Massive connections between miRNAs and diseases have been found by a variety of traditional experiments in the past few years [14,15]. Traditional manual models can infer the connections between miRNA and disease, but which are time-consuming, laborious and high failure rate. Showing the potential relationship between miRNAs and diseases in need of computational methods with effectiveness and stability, as they can obtain increasing reliable miRNA-disease connections [16]
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