ArticleScientific reports2017
Large-scale prediction of microRNA-disease associations by combinatorial prioritization algorithm.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Uncovering miRNA-Disease Associations Through Graph Based Neural Network Representations.Biomedicines · 2026Article
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- Human microRNA similarity in breast cancer.Bioscience reports · 2021Article
- SNFIMCMDA: Similarity Network Fusion and Inductive Matrix Completion for miRNA-Disease Association Prediction.Frontiers in cell and developmental biology · 2021Article
- Biased Random Walk With Restart on Multilayer Heterogeneous Networks for MiRNA-Disease Association Prediction.Frontiers in genetics · 2021Article
- DBMDA: A Unified Embedding for Sequence-Based miRNA Similarity Measure with Applications to Predict and Validate miRNA-Disease Associations.Molecular therapy. Nucleic acids · 2020Article
- Graph regularized LBMC bioinformatics · 2020Article
- Single-nucleotide polymorphism rs17548629 in RIPK1 gene may be associated with lung cancer in a young and middle-aged Han Chinese population.Cancer cell international · 2020Article
- A Computational Study of Potential miRNA-Disease Association Inference Based on Ensemble Learning and Kernel Ridge Regression.Frontiers in bioengineering and biotechnology · 2020Article
- In silico prediction of potential miRNA-disease association using an integrative bioinformatics approach based on kernel fusion.Journal of cellular and molecular medicine · 2020Article
- Article
- Ensemble of decision tree reveals potential miRNA-disease associations.PLoS computational biology · 2019Article
- An integrated framework for the identification of potential miRNA-disease association based on novel negative samples extraction strategy.RNA biology · 2019Article
- Integrating random walk and binary regression to identify novel miRNA-disease association.BMC bioinformatics · 2019Article
- A heterogeneous label propagation approach to explore the potential associations between miRNA and disease.Journal of translational medicine · 2018Article
- Novel Human miRNA-Disease Association Inference Based on Random Forest.Molecular therapy. Nucleic acids · 2018Article
- MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction.PLoS computational biology · 2018Article
- DR2DI: a powerful computational tool for predicting novel drug-disease associations.Journal of computer-aided molecular design · 2018Article
- Predicting microRNA-disease associations using bipartite local models and hubness-aware regression.RNA biology · 2018Article
- A Novel Computational Method for the Identification of Potential miRNA-Disease Association Based on Symmetric Non-negative Matrix Factorization and Kronecker Regularized Least Square.Frontiers in genetics · 2018Article
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3 authors.
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Abstract
Identification of the associations between microRNA molecules and human diseases from large-scale heterogeneous biological data is an important step for understanding the pathogenesis of diseases in microRNA level. However, experimental verification of microRNA-disease associations is expensive and time-consuming. To overcome the drawbacks of conventional experimental methods, we presented a combinatorial prioritization algorithm to predict the microRNA-disease associations. Importantly, our method can be used to predict microRNAs (diseases) associated with the diseases (microRNAs) without the known associated microRNAs (diseases). The predictive performance of our proposed approach was evaluated and verified by the internal cross-validations and external independent validations based on standard association datasets. The results demonstrate that our proposed method achieves the impressive performance for predicting the microRNA-disease association with the Area Under receiver operation characteristic Curve (AUC), 86.93%, which is indeed outperform the previous prediction methods. Particularly, we observed that the ensemble-based method by integrating the predictions of multiple algorithms can give more reliable and robust prediction than the single algorithm, with the AUC score improved to 92.26%. We applied our combinatorial prioritization algorithm to lung neoplasms and breast neoplasms, and revealed their top 30 microRNA candidates, which are in consistent with the published literatures and databases.
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