Abstract
miRNAs are a category of important endogenous non-coding small RNAs and are ubiquitous in eukaryotes. They are widely involved in the regulatory process of post-transcriptional gene expression and play a critical part in the development of human diseases. By utilizing recent advancements in big data technology, using bioinformatics methods to identify causative miRNA becomes a hot spot. In this paper, a method called RNSSLFN is proposed to identify the miRNA-disease associations by reliable negative sample selection and an improved single-hidden layer feedforward neural network (SLFN). It involves, firstly, obtaining integrated similarity for miRNAs and diseases; next, selecting reliable negative samples from unknown miRNA-disease associations via distinguishing up-regulated or down-regulated miRNAs; then, introducing an improved SLFN to solve the prediction task. The experimental results on the latest data sets HMDD v3.2 and the framework of 5-fold cross-validation (CV) show that the average AUC and AUPR of RNSSLFN achieve 0.9316 and 0.9065 m, respectively, which are superior to the other three state-of-the-art methods. Furthermore, in the case studies of 10 common cancers, more than 70% of the top 30 predicted miRNA-disease association pairs are verified in the databases, which further confirms the reliability and effectiveness of the RNSSLFN model. Generally, RNSSLFN in predicting miRNA-disease associations has prodigious potential and extensive foreground.
Highlights
MiRNAs are a kind of small fragment genetic material that widely regulate gene expression in human and other animal cells [1,2]
The comparison of evaluation indexes shows that the performance of RNSSLFN is superior to three different methods
All of the experimental results demonstrate that the RNSSLFN model has significant advantages in revealing the potential associations between miRNAs and diseases
Summary
MiRNAs are a kind of small fragment genetic material that widely regulate gene expression in human and other animal cells [1,2]. Since the discovery of miRNA in 1993 [3], scientists have accumulated the regulatory mechanisms between hundreds of miRNAs and their targets, as well as the role of miRNAs in development, physiology, and disease [4,5,6] These studies shed light on the internal workings of cells and lay the foundation to develop new methods to fight infectious diseases, cancers, and a host of other human diseases. LRMCMDA constructed a mapping network from the known miRNA-disease associations, filtered the negative samples by using the invariance of the mapping network and, transformed the prediction task into a low-rank matrix completion problem. Considering the bipartite nature of the association network, Li et al [19] proposed a collaborative filtering model (CFMDA) to solve the problem of miRNA-disease associations prediction.
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