ArticleCell death & disease2018
EGBMMDA: Extreme Gradient Boosting Machine for MiRNA-Disease Association prediction.
Article in Cell death & disease, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 116 papers.
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Who cites it
116 citing papers in PubMed, 274 citations in OpenAlex.
- Representation learning from similarity networks improves the prediction of disease-associated microRNAs.BMC bioinformatics · 2026Article
- Computational Method Using Attribute-Aware Message Passing and Graph Convolutional Network for Potential miRNA-Disease Association Prediction.International journal of molecular sciences · 2026Article
- MHNNMDA: multi-stage hypergraph neural network for predicting miRNA-disease association types.Journal of computer-aided molecular design · 2026Article
- SHAP based feature selection for machine learning prediction of mycoplasma pneumoniae pneumonia with atelectasis in children.BMC pediatrics · 2026Article
- From biogenesis to deep modeling: a holistic review of miRNA-disease prediction computational methods with experimental comparison.Briefings in bioinformatics · 2026Review
- Disentangled contrastive learning with dynamic intent adaptation for unveiling gene-drug associations.Briefings in bioinformatics · 2025Article
- The art of precision in unveiling hydrogen solubility in bines through data-driven modeling.Scientific reports · 2025Article
- GONNMDA: A Ordered Message Passing GNN Approach for miRNA-Disease Association Prediction.Genes · 2025Article
- Integrating multiple microRNA functional similarity networks for improved disease-microRNA association prediction.Biology methods & protocols · 2025Article
- Machine Learning Approach and Bioinformatics Analysis Discovered Key Genomic Signatures for Hepatitis B Virus-Associated Hepatocyte Remodeling and Hepatocellular Carcinoma.Cancer informatics · 2025Review
- Radiomic signatures of brain metastases on MRI: utility in predicting pathological subtypes of lung cancer.Translational cancer research · 2024Article
- Multi-type classification of lung nodules based on CT radiomics and ensemble learning for diversity weighting.Quantitative imaging in medicine and surgery · 2024Article
- TriFusion enables accurate prediction of miRNA-disease association by a tri-channel fusion neural network.Communications biology · 2024Article
- HGCLAMIR: Hypergraph contrastive learning with attention mechanism and integrated multi-view representation for predicting miRNA-disease associations.PLoS computational biology · 2024Article
- MGCNSS: miRNA-disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy.Briefings in bioinformatics · 2024Article
- Article
- ReHoGCNES-MDA: prediction of miRNA-disease associations using homogenous graph convolutional networks based on regular graph with random edge sampler.Briefings in bioinformatics · 2024Article
- DAEMDA: A Method with Dual-Channel Attention Encoding for miRNA-Disease Association Prediction.Biomolecules · 2023Article
- 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
56 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Associations between microRNAs (miRNAs) and human diseases have been identified by increasing studies and discovering new ones is an ongoing process in medical laboratories. To improve experiment productivity, researchers computationally infer potential associations from biological data, selecting the most promising candidates for experimental verification. Predicting potential miRNA-disease association has become a research area of growing importance. This paper presents a model of Extreme Gradient Boosting Machine for MiRNA-Disease Association (EGBMMDA) prediction by integrating the miRNA functional similarity, the disease semantic similarity, and known miRNA-disease associations. The statistical measures, graph theoretical measures, and matrix factorization results for each miRNA-disease pair were calculated and used to form an informative feature vector. The vector for known associated pairs obtained from the HMDD v2.0 database was used to train a regression tree under the gradient boosting framework. EGBMMDA was the first decision tree learning-based model used for predicting miRNA-disease associations. Respectively, AUCs of 0.9123 and 0.8221 in global and local leave-one-out cross-validation proved the model's reliable performance. Moreover, the 0.9048 ± 0.0012 AUC in fivefold cross-validation confirmed its stability. We carried out three different types of case studies of predicting potential miRNAs related to Colon Neoplasms, Lymphoma, Prostate Neoplasms, Breast Neoplasms, and Esophageal Neoplasms. The results indicated that, respectively, 98%, 90%, 98%, 100%, and 98% of the top 50 predictions for the five diseases were confirmed by experiments. Therefore, EGBMMDA appears to be a useful computational resource for miRNA-disease association prediction.
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