ArticlePLoS computational biology2021
SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization.
Article in PLoS computational biology, 2021. 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.
- LEARNER: A Transfer Learning Method for Low-Rank Matrix Estimation.Statistics in medicine · 2026Article
- MV-HAGCN: Prediction of miRNA-Disease Association Based on Multi-View Hybrid Attention Graph Convolutional Network.International journal of molecular sciences · 2026Article
- sChemNET: a deep learning framework for predicting small molecules targeting microRNA function.Nature communications · 2024Article
- CMFHMDA: a prediction framework for human disease-microbe associations based on cross-domain matrix factorization.Briefings in bioinformatics · 2024Article
- Hessian RegularizedInterdisciplinary sciences, computational life sciences · 2024Article
- GDCL-NcDA: identifying non-coding RNA-disease associations via contrastive learning between deep graph learning and deep matrix factorization.BMC genomics · 2023Article
- Boosted crow search algorithm for handling multi-threshold image problems with application to X-ray images of COVID-19.Expert systems with applications · 2023Article
- Gaussian bare-bone slime mould algorithm: performance optimization and case studies on truss structures.Artificial intelligence review · 2023Article
- A novel bi-directional heterogeneous network selection method for disease and microbial association prediction.BMC bioinformatics · 2022Article
- A message passing framework with multiple data integration for miRNA-disease association prediction.Scientific reports · 2022Article
- SDNN-PPI: self-attention with deep neural network effect on protein-protein interaction prediction.BMC genomics · 2022Article
- Article
- bSRWPSO-FKNN: A boosted PSO with fuzzy K-nearest neighbor classifier for predicting atopic dermatitis disease.Frontiers in neuroinformatics · 2022Article
- Application of Bidirectional Generative Adversarial Networks to Predict Potential miRNAs Associated With Diseases.Frontiers in genetics · 2022Article
- Editorial: Machine learning-based methods for RNA data analysis-Volume II.Frontiers in genetics · 2022Article
- KGDCMI: A New Approach for Predicting circRNA-miRNA Interactions From Multi-Source Information Extraction and Deep Learning.Frontiers in genetics · 2022Article
- Predict potential miRNA-disease associations based on bounded nuclear norm regularization.Frontiers in genetics · 2022Article
- Inferring human miRNA-disease associations via multiple kernel fusion on GCNII.Frontiers in genetics · 2022Article
- Boosting Slime Mould Algorithm for High-Dimensional Gene Data Mining: Diversity Analysis and Feature Selection.Computational and mathematical methods in medicine · 2022Article
- Multi-threshold image segmentation for melanoma based on Kapur's entropy using enhanced ant colony optimization.Frontiers in neuroinformatics · 2022Article
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7 authors.
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Abstract
miRNAs belong to small non-coding RNAs that are related to a number of complicated biological processes. Considerable studies have suggested that miRNAs are closely associated with many human diseases. In this study, we proposed a computational model based on Similarity Constrained Matrix Factorization for miRNA-Disease Association Prediction (SCMFMDA). In order to effectively combine different disease and miRNA similarity data, we applied similarity network fusion algorithm to obtain integrated disease similarity (composed of disease functional similarity, disease semantic similarity and disease Gaussian interaction profile kernel similarity) and integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity and miRNA Gaussian interaction profile kernel similarity). In addition, the L2 regularization terms and similarity constraint terms were added to traditional Nonnegative Matrix Factorization algorithm to predict disease-related miRNAs. SCMFMDA achieved AUCs of 0.9675 and 0.9447 based on global Leave-one-out cross validation and five-fold cross validation, respectively. Furthermore, the case studies on two common human diseases were also implemented to demonstrate the prediction accuracy of SCMFMDA. The out of top 50 predicted miRNAs confirmed by experimental reports that indicated SCMFMDA was effective for prediction of relationship between miRNAs and diseases.
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