ArticleScientific reports2018
Prediction of potential disease-associated microRNAs by composite network based inference.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Improving the Diagnostic Potential of Extracellular miRNAs Coupled to Multiomics Data by Exploiting the Power of Artificial Intelligence.Frontiers in microbiology · 2022Article
- Predicting miRNA-based disease-disease relationships through network diffusion on multi-omics biological data.Scientific reports · 2020Article
- Further Evidence That MicroRNAs Can Play a Role in Hemophilia A Disease Manifestation:Frontiers in cell and developmental biology · 2020Article
- Benchmark of computational methods for predicting microRNA-disease associations.Genome biology · 2019Article
- Network Medicine in Pathobiology.The American journal of pathology · 2019Review
- TMF inhibits miR-29a/Wnt/β-catenin signaling through upregulating Foxo3a activity in osteoarthritis chondrocytes.Drug design, development and therapy · 2019Article
- Factor VIII as a Novel Biomarker for Diagnosis, Prognosis, and Therapy Prediction in Human Cancer and Other Disorders.Avicenna journal of medical biotechnologyReview
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Authors and funding
3 authors.
Funding
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.
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