Abstract
Motivation Increasing studies have demonstrated that many human complex diseases are associated with not only microRNAs, but also long-noncoding RNAs (lncRNAs). LncRNAs and microRNA play significant roles in various biological processes. Therefore, developing effective computational models for predicting novel associations between diseases and lncRNA-miRNA pairs (LMPairs) will be beneficial to not only the understanding of disease mechanisms at lncRNA-miRNA level and the detection of disease biomarkers for disease diagnosis, treatment, prognosis, and prevention, but also the understanding of interactions between diseases and LMPairs at disease level. Results It is well known that genes with similar functions are often associated with similar diseases. In this article, a novel model named PADLMP for predicting associations between diseases and LMPairs is proposed. In this model, a Disease-LncRNA-miRNA (DLM) tripartite network was designed firstly by integrating the lncRNA-disease association network and miRNA-disease association network; then we constructed the disease-LMPairs bipartite association network based on the DLM network and lncRNA-miRNA association network; finally, we predicted potential associations between diseases and LMPairs based on the newly constructed disease-LMPair network. Simulation results show that PADLMP can achieve AUCs of 0.9318, 0.9090 ± 0.0264, and 0.8950 ± 0.0027 in the LOOCV, 2-fold, and 5-fold cross validation framework, respectively, which demonstrate the reliable prediction performance of PADLMP.
Highlights
MicroRNAs are endogenous small and nonencoding RNA molecules, which can regulate gene expression at the posttranscriptional level by combining the 3 untranslated regions (UTRs) of target mRNAs (UTR) and lead the translation inhibited cleavage of the target mRNAs [1]
Accumulating evidences show that the interaction of long-noncoding RNAs (lncRNAs)-miRNAs is involved in the formation of many complex human diseases, such as breast cancer [16]; to our knowledge, there are no prediction models proposed for large scale forecasting the associations between diseases and lncRNA-miRNA pairs (LMPairs)
Based on the existing miRNA-disease associations, lncRNA-disease associations, lncRNA-miRNA interactions, and the assumption that genes with similar functions are often associated with similar diseases, we proposed a novel prediction model PADLMP to infer potential associations between diseases and LMPairs
Summary
MicroRNAs (miRNAs) are endogenous small and nonencoding RNA molecules, which can regulate gene expression at the posttranscriptional level by combining the 3 untranslated regions (UTRs) of target mRNAs (UTR) and lead the translation inhibited cleavage of the target mRNAs [1]. There are researches showing that miRNA-miRNA pairs can work cooperatively to regulate an individual gene or cohort of genes that participate in similar processes [18, 22] Inspired by these existing stateof-the-art methods and ideas for large-scale prediction of the associations between diseases and miRNA-miRNA pairs and based on the reasonable assumption that functionally similar LMPairs tend to be associated with similar diseases, in this paper, a new model named PADLMP is proposed to predict potential associations between diseases and LMPairs. To date, it is the first computational model used to predict disease-LMPairs associations. The results of the prediction show that the PADLMP model is feasible and effective in predicting broad-scale disease-LMPairs associations by considering the topology information of the known diseaseLMPairs dichotomous network
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