ArticleOncotarget2017
MCMDA: Matrix completion for MiRNA-disease association prediction.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers.
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Who cites it
92 citing papers in PubMed.
- RGCNMDA: pathway-bridged relational graph learning with adaptive multi-view fusion for human miRNA-disease association prediction.Bioinformatics (Oxford, England) · 2026Article
- Uncovering miRNA-Disease Associations Through Graph Based Neural Network Representations.Biomedicines · 2026Article
- RLNSF-MDA: reliability-guided graph-regularized matrix factorization for immune-related miRNA-disease association prediction.Frontiers in bioinformatics · 2026Article
- Identifying potential miRNA-disease associations through an accurate matrix completion approach.Briefings in bioinformatics · 2025Article
- Ensemble learning based on matrix completion improves microbe-disease association prediction.Briefings in bioinformatics · 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
- HGTMDA: A Hypergraph Learning Approach with Improved GCN-Transformer for miRNA-Disease Association Prediction.Bioengineering (Basel, Switzerland) · 2024Article
- SCPLPA: An miRNA-disease association prediction model based on spatial consistency projection and label propagation algorithm.Journal of cellular and molecular medicine · 2024Article
- MHGTMDA: Molecular heterogeneous graph transformer based on biological entity graph for miRNA-disease associations prediction.Molecular therapy. Nucleic acids · 2024Article
- Three-layer heterogeneous network based on the integration of CircRNA information for MiRNA-disease association prediction.PeerJ. Computer science · 2024Article
- STAT3 expression in patients with fragility fractures and its effect on the biological function of osteoblasts.Cell and tissue banking · 2023Article
- KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection.BMC bioinformatics · 2023Article
- HLGNN-MDA: Heuristic Learning Based on Graph Neural Networks for miRNA-Disease Association Prediction.International journal of molecular sciences · 2022Article
- JSCSNCP-LMA: a method for predicting the association of lncRNA-miRNA.Scientific reports · 2022Article
- Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for MiRNA-disease association prediction.BMC bioinformatics · 2022Article
- Article
- MDMF: Predicting miRNA-Disease Association Based on Matrix Factorization with Disease Similarity Constraint.Journal of personalized medicine · 2022Article
- MicroRNA-139-5p acts as a suppressor gene for depression by targeting nuclear receptor subfamily 3, group C, member 1.Bioengineered · 2022Article
- Predicting miRNA-Disease Association Based on Neural Inductive Matrix Completion with Graph Autoencoders and Self-Attention Mechanism.Biomolecules · 2022Article
32 more citing papers are in PubMed but not listed here.
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5 authors.
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Abstract
Nowadays, researchers have realized that microRNAs (miRNAs) are playing a significant role in many important biological processes and they are closely connected with various complex human diseases. However, since there are too many possible miRNA-disease associations to analyze, it remains difficult to predict the potential miRNAs related to human diseases without a systematic and effective method. In this study, we developed a Matrix Completion for MiRNA-Disease Association prediction model (MCMDA) based on the known miRNA-disease associations in HMDD database. MCMDA model utilized the matrix completion algorithm to update the adjacency matrix of known miRNA-disease associations and furthermore predict the potential associations. To evaluate the performance of MCMDA, we performed leave-one-out cross validation (LOOCV) and 5-fold cross validation to compare MCMDA with three previous classical computational models (RLSMDA, HDMP, and WBSMDA). As a result, MCMDA achieved AUCs of 0.8749 in global LOOCV, 0.7718 in local LOOCV and average AUC of 0.8767+/-0.0011 in 5-fold cross validation. Moreover, the prediction results associated with colon neoplasms, kidney neoplasms, lymphoma and prostate neoplasms were verified. As a consequence, 84%, 86%, 78% and 90% of the top 50 potential miRNAs for these four diseases were respectively confirmed by recent experimental discoveries. Therefore, MCMDA model is superior to the previous models in that it improves the prediction performance although it only depends on the known miRNA-disease associations.
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