ArticleJournal of cellular and molecular medicine2019
MCLPMDA: A novel method for miRNA-disease association prediction based on matrix completion and label propagation.
Article in Journal of cellular and molecular medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 65 citations in OpenAlex.
- KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection.BMC bioinformatics · 2023Article
- BMPMDA: Prediction of MiRNA-Disease Associations Using a Space Projection Model Based on Block Matrix.Interdisciplinary sciences, computational life sciences · 2023Article
- RNADisease v4.0: an updated resource of RNA-associated diseases, providing RNA-disease analysis, enrichment and prediction.Nucleic acids research · 2023Article
- An efficient model for predicting human diseases through miRNA based on multiple-types of contrastive learning.Frontiers in microbiology · 2023Article
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- Predicting miRNA-Disease Association Based on Neural Inductive Matrix Completion with Graph Autoencoders and Self-Attention Mechanism.Biomolecules · 2022Article
- LE-MDCAP: A Computational Model to Prioritize Causal miRNA-Disease Associations.International journal of molecular sciences · 2021Article
- Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph.BMC medical genomics · 2021Article
- Predicting miRNA-disease associations using improved random walk with restart and integrating multiple similarities.Scientific reports · 2021Article
- SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization.PLoS computational biology · 2021Article
- MNDR v3.0: mammal ncRNA-disease repository with increased coverage and annotation.Nucleic acids research · 2021Article
- Predicting miRNA-Disease Associations Based on Heterogeneous Graph Attention Networks.Frontiers in genetics · 2021Article
- WVMDA: Predicting miRNA-Disease Association Based on Weighted Voting.Frontiers in genetics · 2021Article
- ILPMDA: Predicting miRNA-Disease Association Based on Improved Label Propagation.Frontiers in genetics · 2021Article
- MicroRNA-disease association prediction by matrix tri-factorization.BMC genomics · 2020Article
- Predicting MiRNA-disease associations by multiple meta-paths fusion graph embedding model.BMC bioinformatics · 2020Article
- MCCMF: collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations.BMC bioinformatics · 2020Article
- FCGCNMDA: predicting miRNA-disease associations by applying fully connected graph convolutional networks.Molecular genetics and genomics : MGG · 2020Article
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MiRNAs are a class of small non-coding RNAs that are involved in the development and progression of various complex diseases. Great efforts have been made to discover potential associations between miRNAs and diseases recently. As experimental methods are in general expensive and time-consuming, a large number of computational models have been developed to effectively predict reliable disease-related miRNAs. However, the inherent noise and incompleteness in the existing biological datasets have inevitably 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 matrix completion and label propagation. Specifically, our method first reconstructs a new miRNA/disease similarity matrix by matrix completion algorithm based on known experimentally verified miRNA-disease associations and then utilizes the label propagation algorithm to reliably predict disease-related miRNAs. As a result, MCLPMDA achieved comparable performance under different evaluation metrics and was capable of discovering greater number of true miRNA-disease associations. Moreover, case study conducted on Breast Neoplasms further confirmed the prediction reliability of the proposed method. Taken together, the experimental results clearly demonstrated that MCLPMDA can serve as an effective and reliable tool for miRNA-disease association prediction.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.