ArticlePLoS computational biology2019
Ensemble of decision tree reveals potential miRNA-disease associations.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers.
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Who cites it
80 citing papers in PubMed.
- MHNNMDA: multi-stage hypergraph neural network for predicting miRNA-disease association types.Journal of computer-aided molecular design · 2026Article
- Estimation of onion crop evapotranspiration and crop coefficients using weighing lysimeters and machine learning models in semi-arid region.Scientific reports · 2026Article
- Integrating multiple microRNA functional similarity networks for improved disease-microRNA association prediction.Biology methods & protocols · 2025Article
- Prediction of miRNA-disease associations based on PCA and cascade forest.BMC bioinformatics · 2024Article
- Databases and computational methods for the identification of piRNA-related molecules: A survey.Computational and structural biotechnology journal · 2024Review
- A heterogeneous information network learning model with neighborhood-level structural representation for predicting lncRNA-miRNA interactions.Computational and structural biotechnology journal · 2024Article
- The improved de Bruijn graph for multitask learning: predicting functions, subcellular localization, and interactions of noncoding RNAs.Briefings in bioinformatics · 2024Article
- Structure-inclusive similarity based directed GNN: a method that can control information flow to predict drug-target binding affinity.Bioinformatics (Oxford, England) · 2024Article
- Heterogeneous biomedical entity representation learning for gene-disease association prediction.Briefings in bioinformatics · 2024Article
- MGCNSS: miRNA-disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy.Briefings in bioinformatics · 2024Article
- MHGTMDA: Molecular heterogeneous graph transformer based on biological entity graph for miRNA-disease associations prediction.Molecular therapy. Nucleic acids · 2024Article
- DNI-MDCAP: improvement of causal MiRNA-disease association prediction based on deep network imputation.BMC bioinformatics · 2024Article
- Prediction of miRNAs and diseases association based on sparse autoencoder and MLP.Frontiers in genetics · 2024Article
- MVNMDA: A Multi-View Network Combing Semantic and Global Features for Predicting miRNA-Disease Association.Molecules (Basel, Switzerland) · 2023Article
- Inferring pseudogene-MiRNA associations based on an ensemble learning framework with similarity kernel fusion.Scientific reports · 2023Article
- Prediction of disease-related miRNAs by voting with multiple classifiers.BMC bioinformatics · 2023Article
- Serum microRNA as a potential biomarker for the activity of thyroid eye disease.Scientific reports · 2023Article
- Identifying potential drug-target interactions based on ensemble deep learning.Frontiers in aging neuroscience · 2023Article
- An efficient model for predicting human diseases through miRNA based on multiple-types of contrastive learning.Frontiers in microbiology · 2023Article
- Prediction of miRNA-disease associations in microbes based on graph convolutional networks and autoencoders.Frontiers in microbiology · 2023Article
20 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, increasing associations between microRNAs (miRNAs) and human diseases have been identified. Based on accumulating biological data, many computational models for potential miRNA-disease associations inference have been developed, which saves time and expenditure on experimental studies, making great contributions to researching molecular mechanism of human diseases and developing new drugs for disease treatment. In this paper, we proposed a novel computational method named Ensemble of Decision Tree based MiRNA-Disease Association prediction (EDTMDA), which innovatively built a computational framework integrating ensemble learning and dimensionality reduction. For each miRNA-disease pair, the feature vector was extracted by calculating the statistical measures, graph theoretical measures, and matrix factorization results for the miRNA and disease, respectively. Then multiple base learnings were built to yield many decision trees (DTs) based on random selection of negative samples and miRNA/disease features. Particularly, Principal Components Analysis was applied to each base learning to reduce feature dimensionality and hence remove the noise or redundancy. Average strategy was adopted for these DTs to get final association scores between miRNAs and diseases. In model performance evaluation, EDTMDA showed AUC of 0.9309 in global leave-one-out cross validation (LOOCV) and AUC of 0.8524 in local LOOCV. Additionally, AUC of 0.9192+/-0.0009 in 5-fold cross validation proved the model's reliability and stability. Furthermore, three types of case studies for four human diseases were implemented. As a result, 94% (Esophageal Neoplasms), 86% (Kidney Neoplasms), 96% (Breast Neoplasms) and 88% (Carcinoma Hepatocellular) of top 50 predicted miRNAs were confirmed by experimental evidences in literature.
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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 Socratic graph.