Abstract
In recent years, more and more studies have shown that miRNAs can affect a variety of biological processes. It is important for disease prevention, treatment, diagnosis, and prognosis to study the relationships between human diseases and miRNAs. However, traditional experimental methods are time-consuming and labour-intensive. Hence, in this paper, a novel neighborhood-based computational model called NBMDA is proposed for predicting potential miRNA-disease associations. Due to the fact that known miRNA-disease associations are very rare and many diseases (or miRNAs) are associated with only one or a few miRNAs (or diseases), in NBMDA, the K-nearest neighbor (KNN) method is utilized as a recommendation algorithm based on known miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases to improve its prediction accuracy. And simulation results demonstrate that NBMDA can effectively infer miRNA-disease associations with higher accuracy compared with previous state-of-the-art methods. Moreover, independent case studies of esophageal neoplasms, breast neoplasms and colon neoplasms are further implemented, and as a result, there are 47, 48, and 48 out of the top 50 predicted miRNAs having been successfully confirmed by the previously published literatures, which also indicates that NBMDA can be utilized as a powerful tool to study the relationships between miRNAs and diseases.
Highlights
MiRNAs are one kind of small RNAs with the length of about 20–24 nucleotides that can regulate the expression of posttranscriptional genes, and each miRNA may have multiple target genes that can be regulated by multiple miRNAs as well [1,2,3,4]
To further evaluate the predictive performance of NBMDA, we compared NBMDA with two state-of-the-art computational methods such as WBSMDA [37] and RLSMDA [38] in terms of global LOOCV and 5-fold CV as well, and the simulation result is shown in the above Figure 2
Experimental results show that NBMDA can achieve reliable areas under ROC curves (AUCs) of 0.8983/ 0.8153 and 0.8975 in the frameworks of global LOOCV and 5-fold CV, respectively, which are much better than the AUCs achieved by state-of-the-art prediction models such as WBSMDA and RLSMDA
Summary
MiRNAs are one kind of small RNAs with the length of about 20–24 nucleotides that can regulate the expression of posttranscriptional genes, and each miRNA may have multiple target genes that can be regulated by multiple miRNAs as well [1,2,3,4]. Emerging evidences have implied as well that miRNAs can affect the occurrence and development of various tumors by regulating the signaling pathways in which their target genes are involved and play a role similar to oncogenes or tumor suppressor genes [9]. MiR-203 can inhibit the formation of esophageal tumors [10], miR-328 is a key oncogene in hepatocellular carcinoma, and its expression level will be significantly upregulated and downregulated in hepatocellular carcinoma tissues [11]. MiR-143 and miR-145 are expressed at low levels in esophageal cancer and gastric cancer, which mean that the downregulation of these two kinds of miRNAs can be considered as a potential biomarker for related tumors [12]. The exploration of potential relationships between miRNAs and diseases will have important significance for disease prevention, treatment, diagnosis, and prognosis [13,14,15]
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