ArticleiScience2021
DANE-MDA: Predicting microRNA-disease associations via deep attributed network embedding.
Article in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- From biogenesis to deep modeling: a holistic review of miRNA-disease prediction computational methods with experimental comparison.Briefings in bioinformatics · 2026Review
- A multi-scale biological network framework for discovering MiRNA-Disease associations.Frontiers in bioinformatics · 2026Article
- Predicting miRNA-disease associations based on PPMI and attention network.BMC bioinformatics · 2023Article
- Article
- SMMDA: Predicting miRNA-Disease Associations by Incorporating Multiple Similarity Profiles and a Novel Disease Representation.Biology · 2022Article
- Hierarchical graph attention network for miRNA-disease association prediction.Molecular therapy : the journal of the American Society of Gene Therapy · 2022Article
- Predicting miRNA-disease associations based on multi-view information fusion.Frontiers in genetics · 2022Article
- Editorial: Machine learning-based methods for RNA data analysis-Volume II.Frontiers in genetics · 2022Article
- Heterogeneous Types of miRNA-Disease Associations Stratified by Multi-Layer Network Embedding and Prediction.Biomedicines · 2021Article
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Authors and funding
5 authors.
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Abstract
Predicting the microRNA-disease associations by using computational methods is conductive to the efficiency of costly and laborious traditional bio-experiments. In this study, we propose a computational machine learning-based method (DANE-MDA) that preserves integrated structure and attribute features via deep attributed network embedding to predict potential miRNA-disease associations. Specifically, the integrated features are extracted by using deep stacked auto-encoder on the diverse orders of matrixes containing structure and attribute information and are then trained by using random forest classifier. Under 5-fold cross-validation experiments, DANE-MDA yielded average accuracy, sensitivity, and AUC at 85.59%, 84.23%, and 0.9264 in term of HMDD v3.0 dataset, and 83.21%, 80.39%, and 0.9113 in term of HMDD v2.0 dataset, respectively. Additionally, case studies on breast, colon, and lung neoplasms related disease show that 47, 47, and 46 of the top 50 miRNAs can be predicted and retrieved in the other database.
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Registered trials
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