ArticleScientific reports2017
A deep ensemble model to predict miRNA-disease association.
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 40 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
40 citing papers in PubMed.
- Unique circulating miRNAome signatures captured with distinct molecular states in Lynch syndrome and sporadic colorectal cancer.Molecular oncology · 2026Article
- HybridGNN: a graph neural network approach for human miRNA-disease association prediction.Bioinformatics (Oxford, England) · 2026Article
- MicroRNAs in oncology: a translational perspective in the era of AI.Nature reviews. Clinical oncology · 2026Review
- From biogenesis to deep modeling: a holistic review of miRNA-disease prediction computational methods with experimental comparison.Briefings in bioinformatics · 2026Review
- MicroRNA-661 Suppresses Proliferation and Migration of Pulmonary Artery Smooth Muscle Cells and Endothelial Cells by Targeting CCND2.Biologics : targets & therapy · 2026Article
- A multi-scale biological network framework for discovering MiRNA-Disease associations.Frontiers in bioinformatics · 2026Article
- Establishing a GRU-GCN coordination-based prediction model for miRNA-disease associations.BMC genomic data · 2025Article
- TriFusion enables accurate prediction of miRNA-disease association by a tri-channel fusion neural network.Communications biology · 2024Article
- HGTMDA: A Hypergraph Learning Approach with Improved GCN-Transformer for miRNA-Disease Association Prediction.Bioengineering (Basel, Switzerland) · 2024Article
- HGCLAMIR: Hypergraph contrastive learning with attention mechanism and integrated multi-view representation for predicting miRNA-disease associations.PLoS computational biology · 2024Article
- DNI-MDCAP: improvement of causal MiRNA-disease association prediction based on deep network imputation.BMC bioinformatics · 2024Article
- HLGNN-MDA: Heuristic Learning Based on Graph Neural Networks for miRNA-Disease Association Prediction.International journal of molecular sciences · 2022Article
- TLNPMD: Prediction of miRNA-Disease Associations Based on miRNA-Drug-Disease Three-Layer Heterogeneous Network.Molecules (Basel, Switzerland) · 2022Article
- DNRLCNN: A CNN Framework for Identifying MiRNA-Disease Associations Using Latent Feature Matrix Extraction with Positive Samples.Interdisciplinary sciences, computational life sciences · 2022Article
- Dual Attention Mechanisms and Feature Fusion Networks Based Method for Predicting LncRNA-Disease Associations.Interdisciplinary sciences, computational life sciences · 2022Article
- UFold: fast and accurate RNA secondary structure prediction with deep learning.Nucleic acids research · 2022Article
- Machine learning in the development of targeting microRNAs in human disease.Frontiers in genetics · 2022Review
- Classification of Breast Cancer and Breast Neoplasm Scenarios Based on Machine Learning and Sequence Features from lncRNAs-miRNAs-Diseases Associations.Interdisciplinary sciences, computational life sciences · 2021Article
- Bipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations.BMC bioinformatics · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cumulative evidence from biological experiments has confirmed that microRNAs (miRNAs) are related to many types of human diseases through different biological processes. It is anticipated that precise miRNA-disease association prediction could not only help infer potential disease-related miRNA but also boost human diagnosis and disease prevention. Considering the limitations of previous computational models, a more effective computational model needs to be implemented to predict miRNA-disease associations. In this work, we first constructed a human miRNA-miRNA similarity network utilizing miRNA-miRNA functional similarity data and heterogeneous miRNA Gaussian interaction profile kernel similarities based on the assumption that similar miRNAs with similar functions tend to be associated with similar diseases, and vice versa. Then, we constructed disease-disease similarity using disease semantic information and heterogeneous disease-related interaction data. We proposed a deep ensemble model called DeepMDA that extracts high-level features from similarity information using stacked autoencoders and then predicts miRNA-disease associations by adopting a 3-layer neural network. In addition to five-fold cross-validation, we also proposed another cross-validation method to evaluate the performance of the model. The results show that the proposed model is superior to previous methods with high robustness.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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 OpenQuestion graph.