Abstract
In recent years, microRNAs (miRNAs) are attracting an increasing amount of researchers’ attention, as accumulating studies show that miRNAs play important roles in various basic biological processes and that dysregulation of miRNAs is connected with diverse human diseases, particularly cancers. However, the experimental methods to identify associations between miRNAs and diseases remain costly and laborious. In this study, we developed a computational method named Network Distance Analysis for MiRNA‐Disease Association prediction (NDAMDA) which could effectively predict potential miRNA‐disease associations. The highlight of this method was the use of not only the direct network distance between 2 miRNAs (diseases) but also their respective mean network distances to all other miRNAs (diseases) in the network. The model's reliable performance was certified by the AUC of 0.8920 in global leave‐one‐out cross‐validation (LOOCV), 0.8062 in local LOOCV and the average AUCs of 0.8935 ± 0.0009 in fivefold cross‐validation. Moreover, we applied NDAMDA to 3 different case studies to predict potential miRNAs related to breast neoplasms, lymphoma, oesophageal neoplasms, prostate neoplasms and hepatocellular carcinoma. Results showed that 86%, 72%, 86%, 86% and 84% of the top 50 predicted miRNAs were supported by experimental association evidence. Therefore, NDAMDA is a reliable method for predicting disease‐related miRNAs.
Highlights
MicroRNAs are a class of non-coding RNAs which play regulatory roles in gene expressions by binding to the complementary regions of messenger transcripts to repress their translation or regulate degradation.[1,2,3] Asince the lin-4 and let-7 were discovered in Caenorhabditis elegans,[4] over 30 000 mature miRNAs have been found from 206 species.[5]
Based on the assumption that functional similar miRNAs tend to be associated with similar diseases and vice versa, we developed the model of Network Distance Analysis for MiRNA-Disease Association prediction (NDAMDA)
We described each disease as a directed acyclic graph (DAG) with the help of the disease MeSH descriptors downloaded from the National Library of Medicine.[33]
Summary
MicroRNAs (miRNAs) are a class of non-coding RNAs which play regulatory roles in gene expressions by binding to the complementary regions of messenger transcripts to repress their translation or regulate degradation.[1,2,3] Asince the lin-4 and let-7 were discovered in Caenorhabditis elegans,[4] over 30 000 mature miRNAs have been found from 206 species.[5]. The members in the same miRNA family or cluster were assigned higher weights because they were usually transcribed together and were more likely to be associated with similar diseases This method had some limitations: on one hand, HDMP could not be applied to the new diseases which did not have any known related miRNAs; on the other hand, HDMP did not make full use of global network similarity information. Negative associations needed for training the model were hard to obtain and the prediction of supervised classifier such as SVM could be inaccurate To address this problem, Chen et al[24] proposed a semi-supervised method named RLSMDA. We obtained 42 confirmed miRNAs in the top 50 candidate miRNAs for hepatocellular carcinoma based on the previous version of HMDD, further suggesting that this model have a good performance on different input dataset
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