ArticleFrontiers in genetics2019
Identifying Potential miRNAs-Disease Associations With Probability Matrix Factorization.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- EMGMDA: a multi-modal graph neural framework for robust prediction of miRNA-disease associations.BMC genomics · 2026Article
- MAGMDA: a multi-order adaptive graph-based miRNA-disease association prediction model.BMC bioinformatics · 2026Article
- HHOMR: a hybrid high-order moment residual model for miRNA-disease association prediction.Briefings in bioinformatics · 2024Article
- EMCMDA: predicting miRNA-disease associations via efficient matrix completion.Scientific reports · 2024Article
- DAE-CFR: detecting microRNA-disease associations using deep autoencoder and combined feature representation.BMC bioinformatics · 2024Article
- Hessian RegularizedInterdisciplinary sciences, computational life sciences · 2024Article
- Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network.BMC genomics · 2024Article
- KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection.BMC bioinformatics · 2023Article
- Prediction of miRNA-disease associations in microbes based on graph convolutional networks and autoencoders.Frontiers in microbiology · 2023Article
- Computational drug repositioning using similarity constrained weight regularization matrix factorization: A case of COVID-19.Journal of cellular and molecular medicine · 2022Article
- Article
- Plasma Exosomes Transfer miR-885-3p Targeting the AKT/NFκB Signaling Pathway to Improve the Sensitivity of Intravenous Glucocorticoid Therapy Against Graves Ophthalmopathy.Frontiers in immunology · 2022Article
- Predict potential miRNA-disease associations based on bounded nuclear norm regularization.Frontiers in genetics · 2022Article
- Predicting miRNA-disease associations using improved random walk with restart and integrating multiple similarities.Scientific reports · 2021Article
- HFS-SLPEE: A Novel Hierarchical Feature Selection and Second Learning Probability Error Ensemble Model for Precision Cancer Diagnosis.Frontiers in cell and developmental biology · 2021Article
- Predicting Pseudogene-miRNA Associations Based on Feature Fusion and Graph Auto-Encoder.Frontiers in genetics · 2021Review
- A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method.PloS one · 2021Article
- ILPMDA: Predicting miRNA-Disease Association Based on Improved Label Propagation.Frontiers in genetics · 2021Article
- Human Microbe-Disease Association Prediction by a Novel Double-Ended Random Walk with Restart.BioMed research international · 2020Article
- Indicator Regularized Non-Negative Matrix Factorization Method-Based Drug Repurposing for COVID-19.Frontiers in immunology · 2020Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, miRNAs have been verified to play an irreplaceable role in biological processes associated with human disease. Discovering potential disease-related miRNAs helps explain the underlying pathogenesis of the disease at the molecular level. Given the high cost and labor intensity of biological experiments, computational predictions will be an indispensable alternative. Therefore, we design a new model called probability matrix factorization (PMFMDA). Specifically, we first integrate miRNA and disease similarity. Next, the known association matrix and integrated similarity matrix are utilized to construct a probability matrix factorization algorithm to identify potentially relevant miRNAs for disease. We find that PMFMDA achieves reliable performance in the frameworks of global leave-one-out cross validation (LOOCV) and 5-fold cross validation (AUCs are 0.9237 and 0.9187, respectively) in the HMDD (V2.0) dataset, significantly outperforming a few state-of-the-art methods including CMFMDA, IMCMDA, NCPMDA, RLSMDA, and RWRMDA. In addition, case studies show that PMFMDA has good predictive performance for new associations, and the evidence can be identified by literature mining.
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