ArticleMolecular therapy. Nucleic acids2018
Novel Human miRNA-Disease Association Inference Based on Random Forest.
Article in Molecular therapy. Nucleic acids, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.
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65 citing papers in PubMed.
- Identification of clinical and urine biomarkers for uncomplicated urinary tract infection using machine learning algorithms.Scientific reports · 2019Trial
- Decoding epilepsy's molecular blueprint: Machine learning unravels transcriptomic subtypes and regulatory networks.Epilepsia · 2026Article
- MV-HAGCN: Prediction of miRNA-Disease Association Based on Multi-View Hybrid Attention Graph Convolutional Network.International journal of molecular sciences · 2026Article
- EMGMDA: a multi-modal graph neural framework for robust prediction of miRNA-disease associations.BMC genomics · 2026Article
- Advanced Bayesian kernel machine regression for large-scale exposome studies: Making the impossible possible.Innovation (Cambridge (Mass.)) · 2026Article
- From biogenesis to deep modeling: a holistic review of miRNA-disease prediction computational methods with experimental comparison.Briefings in bioinformatics · 2026Review
- GONNMDA: A Ordered Message Passing GNN Approach for miRNA-Disease Association Prediction.Genes · 2025Article
- Review
- Integrative machine learning and Mendelian randomization identify causal laboratory biomarkers for coronary artery lesions in Kawasaki disease: a prospective study.Frontiers in genetics · 2025Article
- Article
- Prediction of miRNA-disease associations based on PCA and cascade forest.BMC bioinformatics · 2024Article
- A method for miRNA diffusion association prediction using machine learning decoding of multi-level heterogeneous graph Transformer encoded representations.Scientific reports · 2024Article
- 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
- HGCLAMIR: Hypergraph contrastive learning with attention mechanism and integrated multi-view representation for predicting miRNA-disease associations.PLoS computational biology · 2024Article
- DAE-CFR: detecting microRNA-disease associations using deep autoencoder and combined feature representation.BMC bioinformatics · 2024Article
- MGCNSS: miRNA-disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy.Briefings in bioinformatics · 2024Article
- Hessian RegularizedInterdisciplinary sciences, computational life sciences · 2024Article
- KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection.BMC bioinformatics · 2023Article
- Predicting miRNA-disease associations based on PPMI and attention network.BMC bioinformatics · 2023Article
5 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
Since the first microRNA (miRNA) was discovered, a lot of studies have confirmed the associations between miRNAs and human complex diseases. Besides, obtaining and taking advantage of association information between miRNAs and diseases play an increasingly important role in improving the treatment level for complex diseases. However, due to the high cost of traditional experimental methods, many researchers have proposed different computational methods to predict potential associations between miRNAs and diseases. In this work, we developed a computational model of Random Forest for miRNA-disease association (RFMDA) prediction based on machine learning. The training sample set for RFMDA was constructed according to the human microRNA disease database (HMDD) version (v.)2.0, and the feature vectors to represent miRNA-disease samples were defined by integrating miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity. The Random Forest algorithm was first employed to infer miRNA-disease associations. In addition, a filter-based method was implemented to select robust features from the miRNA-disease feature set, which could efficiently distinguish related miRNA-disease pairs from unrelated miRNA-disease pairs. RFMDA achieved areas under the curve (AUCs) of 0.8891, 0.8323, and 0.8818 ± 0.0014 under global leave-one-out cross-validation, local leave-one-out cross-validation, and 5-fold cross-validation, respectively, which were higher than many previous computational models. To further evaluate the accuracy of RFMDA, we carried out three types of case studies for four human complex diseases. As a result, 43 (esophageal neoplasms), 46 (lymphoma), 47 (lung neoplasms), and 48 (breast neoplasms) of the top 50 predicted disease-related miRNAs were verified by experiments in different kinds of case studies. The results of cross-validation and case studies indicated that RFMDA is a reliable model for predicting miRNA-disease associations.
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