ArticleBioinformatics (Oxford, England)2018
A non-negative matrix factorization based method for predicting disease-associated miRNAs in miRNA-disease bilayer network.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- DeepLCRmiRNA: A Hybrid Neural Network Approach for Identifying Lung Cancer-Associated miRNAs.Current gene therapy · 2025Article
- TriFusion enables accurate prediction of miRNA-disease association by a tri-channel fusion neural network.Communications biology · 2024Article
- Article
- HGSMDA: miRNA-Disease Association Prediction Based on HyperGCN and Sørensen-Dice Loss.Non-coding RNA · 2024Article
- GDCL-NcDA: identifying non-coding RNA-disease associations via contrastive learning between deep graph learning and deep matrix factorization.BMC genomics · 2023Article
- Prediction of miRNA-Disease Associations by Cascade Forest Model Based on Stacked Autoencoder.Molecules (Basel, Switzerland) · 2023Article
- KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection.BMC bioinformatics · 2023Article
- A clustering-based sampling method for miRNA-disease association prediction.Frontiers in genetics · 2022Article
- Radiomics of Contrast-Enhanced Computed Tomography: A Potential Biomarker for Pretreatment Prediction of the Response toFrontiers in cell and developmental biology · 2022Article
- Predicting miRNA-Disease Associations Based on Heterogeneous Graph Attention Networks.Frontiers in genetics · 2021Article
- Comparative analysis of similarity measurements in miRNAs with applications to miRNA-disease association predictions.BMC bioinformatics · 2020Article
- Graph regularized LBMC bioinformatics · 2020Article
- MSCHLMDA: Multi-Similarity Based Combinative Hypergraph Learning for Predicting MiRNA-Disease Association.Frontiers in genetics · 2020Article
- LSGSP: a novel miRNA-disease association prediction model using a Laplacian score of the graphs and space projection federated method.RSC advances · 2019Article
- Article
- Prediction of Disease-related microRNAs through Integrating Attributes of microRNA Nodes and Multiple Kinds of Connecting Edges.Molecules (Basel, Switzerland) · 2019Article
- Inferring the Disease-Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks.International journal of molecular sciences · 2019Article
- Long Noncoding RNA and Protein Interactions: From Experimental Results to Computational Models Based on Network Methods.International journal of molecular sciences · 2019Article
- Bipartite Heterogeneous Network Method Based on Co-neighbor for MiRNA-Disease Association Prediction.Frontiers in genetics · 2019Article
- Dual Convolutional Neural Network Based Method for Predicting Disease-Related miRNAs.International journal of molecular sciences · 2018Article
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7 authors.
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Abstract
motivationIdentification of disease-associated miRNAs (disease miRNAs) is critical for understanding disease etiology and pathogenesis. Since miRNAs exert their functions by regulating the expression of their target mRNAs, several methods based on the target genes were proposed to predict disease miRNA candidates. They achieved only limited success as they all suffered from the high false-positive rate of target prediction results. Alternatively, other prediction methods were based on the observation that miRNAs with similar functions tend to be associated with similar diseases and vice versa. The methods exploited the information about miRNAs and diseases, including the functional similarities between miRNAs, the similarities between diseases, and the associations between miRNAs and diseases. However, how to integrate the multiple kinds of information completely and consider the biological characteristic of disease miRNAs is a challenging problem.
resultsWe constructed a bilayer network to represent the complex relationships among miRNAs, among diseases and between miRNAs and diseases. We proposed a non-negative matrix factorization based method to rank, so as to predict, the disease miRNA candidates. The method integrated the miRNA functional similarity, the disease similarity and the miRNA-disease associations seamlessly, which exploited the complex relationships within the bilayer network and the consensus relationship between multiple kinds of information. Considering the correlation between the candidates related to various diseases, it predicted their respective candidates for all the diseases simultaneously. In addition, the sparseness characteristic of disease miRNAs was introduced to generate more reliable prediction model that excludes those noisy candidates. The results on 15 common diseases showed a superior performance of the new method for not only well-characterized diseases but also new ones. A detailed case study on breast neoplasms, colorectal neoplasms, lung neoplasms and 32 other diseases demonstrated the ability of the method for discovering potential disease miRNAs. AVAILABILITY AND IMPLEMENTATION: The web service for the new method and the list of predicted candidates for all the diseases are available at http://www.bioinfolab.top. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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