ArticleCells2020
Improved Prediction of miRNA-Disease Associations Based on Matrix Completion with Network Regularization.
Article in Cells, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 46 citations in OpenAlex.
- Identification of miRNA biomarkers for essential hypertension in small samples based on MPGAM.Scientific reports · 2025Article
- Neighborhood-Regularized Matrix Factorization for lncRNA-Disease Association Identification.International journal of molecular sciences · 2025Article
- DeepWalk-Based Graph Embeddings for miRNA-Disease Association Prediction Using Deep Neural Network.Biomedicines · 2025Article
- Graph Convolutional Network with Neural Collaborative Filtering for Predicting miRNA-Disease Association.Biomedicines · 2025Article
- ACLNDA: an asymmetric graph contrastive learning framework for predicting noncoding RNA-disease associations in heterogeneous graphs.Briefings in bioinformatics · 2024Article
- Investigation of the Potential Mechanism of Compound Dragon's Blood Capsule against Myocardial Ischemia Based on Network Pharmacology.Combinatorial chemistry & high throughput screening · 2024Article
- Intelligent Alzheimer's Diseases Gene Association Prediction Model Using Deep Regulatory Genomic Neural Networks.Journal of Alzheimer's disease reports · 2024Article
- Network-Pharmacology-Based Study on Active Phytochemicals and Molecular Mechanism of Cnidii Fructus in Treating Colorectal Cancer.Combinatorial chemistry & high throughput screening · 2024Article
- MHCLMDA: multihypergraph contrastive learning for miRNA-disease association prediction.Briefings in bioinformatics · 2023Article
- KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection.BMC bioinformatics · 2023Article
- iPiDA-SWGCN: Identification of piRNA-disease associations based on Supplementarily Weighted Graph Convolutional Network.PLoS computational biology · 2023Article
- microRNA Bioinformatics.Cells · 2022Article
- Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for MiRNA-disease association prediction.BMC bioinformatics · 2022Article
- Predicting miRNA-disease associations based on graph attention network with multi-source information.BMC bioinformatics · 2022Article
- Article
- MDMF: Predicting miRNA-Disease Association Based on Matrix Factorization with Disease Similarity Constraint.Journal of personalized medicine · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The identification of potential microRNA (miRNA)-disease associations enables the elucidation of the pathogenesis of complex human diseases owing to the crucial role of miRNAs in various biologic processes and it yields insights into novel prognostic markers. In the consideration of the time and costs involved in wet experiments, computational models for finding novel miRNA-disease associations would be a great alternative. However, computational models, to date, are biased towards known miRNA-disease associations; this is not suitable for rare miRNAs (i.e., miRNAs with a few known disease associations) and uncommon diseases (i.e., diseases with a few known miRNA associations). This leads to poor prediction accuracies. The most straightforward way of improving the performance is by increasing the number of known miRNA-disease associations. However, due to lack of information, increasing attention has been paid to developing computational models that can handle insufficient data via a technical approach. In this paper, we present a general framework-improved prediction of miRNA-disease associations (IMDN)-based on matrix completion with network regularization to discover potential disease-related miRNAs. The success of adopting matrix factorization is demonstrated by its excellent performance in recommender systems. This approach considers a miRNA network as additional implicit feedback and makes predictions for disease associations relevant to a given miRNA based on its direct neighbors. Our experimental results demonstrate that IMDN achieved excellent performance with reliable area under the receiver operating characteristic (ROC) area under the curve (AUC) values of 0.9162 and 0.8965 in the frameworks of global and local leave-one-out cross-validations (LOOCV), respectively. Further, case studies demonstrated that our method can not only validate true miRNA-disease associations but also suggest novel disease-related miRNA candidates.
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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.