ArticleRNA biology2018
GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods.
Article in RNA biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- From RNA design to delivery: Computational strategies for functional RNA therapeutics.Bioactive materials · 2027Review
- MHNNMDA: multi-stage hypergraph neural network for predicting miRNA-disease association types.Journal of computer-aided molecular design · 2026Article
- iSnoDi-LSGT: identifying snoRNA-disease associations based on local similarity constraints and global topological constraints.RNA (New York, N.Y.) · 2022Article
- DNRLCNN: A CNN Framework for Identifying MiRNA-Disease Associations Using Latent Feature Matrix Extraction with Positive Samples.Interdisciplinary sciences, computational life sciences · 2022Article
- RSCMDA: Prediction of Potential miRNA-Disease Associations Based on a Robust Similarity Constraint Learning Method.Interdisciplinary sciences, computational life sciences · 2021Article
- iCDA-CMG: identifying circRNA-disease associations by federating multi-similarity fusion and collective matrix completion.Molecular genetics and genomics : MGG · 2021Article
- Heterogeneous Graph Convolutional Networks and Matrix Completion for miRNA-Disease Association Prediction.Frontiers in bioengineering and biotechnology · 2020Article
- LSGSP: a novel miRNA-disease association prediction model using a Laplacian score of the graphs and space projection federated method.RSC advances · 2019Article
- Self-Weighted Multi-Kernel Multi-Label Learning for Potential miRNA-Disease Association Prediction.Molecular therapy. Nucleic acids · 2019Article
- Article
- Prediction of Potential Disease-Associated MicroRNAs by Using Neural Networks.Molecular therapy. Nucleic acids · 2019Article
- Adaptive multi-view multi-label learning for identifying disease-associated candidate miRNAs.PLoS computational biology · 2019Article
- MCLPMDA: A novel method for miRNA-disease association prediction based on matrix completion and label propagation.Journal of cellular and molecular medicine · 2019Article
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6 authors.
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Abstract
Recently, increasing studies have shown that miRNAs are involved in the development and progression of various complex diseases. Consequently, predicting potential miRNA-disease associations makes an important contribution to understanding the pathogenesis of diseases, developing new drugs as well as designing individualized diagnostic and therapeutic approaches for different human diseases. Nonetheless, the inherent noise and incompleteness in the existing biological datasets have limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for miRNA-disease association prediction based on global linear neighborhoods (GLNMDA). Specifically, our method obtains a new miRNA/disease similarity matrix by linearly reconstructing each miRNA/disease according to the known experimentally verified miRNA-disease associations. We then adopt label propagation to infer the potential associations between miRNAs and diseases. As a result, GLNMDA achieved reliable performance in the frameworks of both local and global LOOCV (AUCs of 0.867 and 0.929, respectively) and 5-fold cross validation (average AUC of 0.926). Case studies on five common human diseases further confirmed the utility of our method in discovering latent miRNA-disease pairs. Taken together, GLNMDA could serve as a reliable computational tool for miRNA-disease association prediction.
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