Abstract
MicroRNAs (miRNAs) act a significant role in multiple biological processes and their associations with the development of all kinds of complex diseases are much close. In the research area of biology, medicine, and bioinformatics, prediction of potential miRNA-disease associations (MDAs) on the base of a variety of heterogeneous biological datasets in a short time is an important subject. Therefore, we proposed the model of Composite Network based inference for MiRNA-Disease Association prediction (CNMDA) through applying random walk to a multi-level composite network constructed by heterogeneous dataset of disease, long noncoding RNA (lncRNA) and miRNA. The results showed that CNMDA achieved an AUC of 0.8547 in leave-one-out cross validation and an AUC of 0.8533+/−0.0009 in 5-fold cross validation. In addition, we employed CNMDA to infer novel miRNAs for kidney neoplasms, breast neoplasms and lung neoplasms on the base of HMDD v2.0. Also, we employed the approach for lung neoplasms on the base of HMDD v1.0 and for breast neoplasms that have no known related miRNAs. It was found that CNMDA could be seen as an applicable tool for potential MDAs prediction.
Highlights
MicroRNAs is a kind of short noncoding RNA molecules with about 22 nucleotides in length which can regulate complementary messenger RNAs1
We carried out leave-one-out cross validation (LOOCV) and 5-fold cross validation to assess CNMDA’s prediction accuracy according to HMDD v2.029 and made comparison between CNMDA and four other classical computational models: RLSMDA23, HDMP21, WBSMDA24 and RKNNMDA17 (See Fig. 1)
In LOOCV, test sample is one of the 5430 miRNA-disease associations (MDAs); training samples are the rest of 5429 known MDAs; candidate samples are those unlabeled 184155 miRNA-disease pairs
Summary
In the first case study, CNMDA was employed to predict KN-related miRNAs based on HMDD v2.0. Another two reliable MDA databases (dbDEMC and miR2Disease) would be utilized to validate the top 50 identified outcomes. We put forward the computing method of CNMDA to infer novel MDAs. In the model, we implemented RWR on a multi-level composite network that was built through combining collected and calculated data (ISD, ISM, GIPKS for lncRNAs, experimentally validated MDAs, MLIs and LDAs). CNMDA could identify novel diseases that have no known associated miRNAs. At last, the implementation of CNMDA only needs positive samples as training data. The current forecasting precision still needs to be improved according to the evaluation of LOOCV
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