ArticleMolecular therapy : the journal of the American Society of Gene Therapy2021
DF-MDA: An effective diffusion-based computational model for predicting miRNA-disease association.
Article in Molecular therapy : the journal of the American Society of Gene Therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 21 citations in OpenAlex.
- AMFCL: Predicting miRNA-Disease Associations Through Adaptive Multi-source Modality Fusion and Contrastive Learning.Interdisciplinary sciences, computational life sciences · 2026Article
- Synchronous Mutual Learning Network and Asynchronous Multi-Scale Embedding Network for miRNA-Disease Association Prediction.Interdisciplinary sciences, computational life sciences · 2024Article
- TriFusion enables accurate prediction of miRNA-disease association by a tri-channel fusion neural network.Communications biology · 2024Article
- Artificial intelligence in lung cancer: current applications, future perspectives, and challenges.Frontiers in oncology · 2024Review
- MSPEDTI: Prediction of Drug-Target Interactions via Molecular Structure with Protein Evolutionary Information.Biology · 2022Article
- Hierarchical graph attention network for miRNA-disease association prediction.Molecular therapy : the journal of the American Society of Gene Therapy · 2022Article
- DeepWalk based method to predict lncRNA-miRNA associations via lncRNA-miRNA-disease-protein-drug graph.BMC bioinformatics · 2022Review
- Predict potential miRNA-disease associations based on bounded nuclear norm regularization.Frontiers in genetics · 2022Article
- Heterogeneous Types of miRNA-Disease Associations Stratified by Multi-Layer Network Embedding and Prediction.Biomedicines · 2021Article
- Expectile Neural Networks for Genetic Data Analysis of Complex Diseases.IEEE/ACM transactions on computational biology and bioinformaticsArticle
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is reported that microRNAs (miRNAs) play an important role in various human diseases. However, the mechanisms of miRNA in these diseases have not been fully understood. Therefore, detecting potential miRNA-disease associations has far-reaching significance for pathological development and the diagnosis and treatment of complex diseases. In this study, we propose a novel diffusion-based computational method, DF-MDA, for predicting miRNA-disease association based on the assumption that molecules are related to each other in human physiological processes. Specifically, we first construct a heterogeneous network by integrating various known associations among miRNAs, diseases, proteins, long non-coding RNAs (lncRNAs), and drugs. Then, more representative features are extracted through a diffusion-based machine-learning method. Finally, the Random Forest classifier is adopted to classify miRNA-disease associations. In the 5-fold cross-validation experiment, the proposed model obtained the average area under the curve (AUC) of 0.9321 on the HMDD v3.0 dataset. To further verify the prediction performance of the proposed model, DF-MDA was applied in three significant human diseases, including lymphoma, lung neoplasms, and colon neoplasms. As a result, 47, 46, and 47 out of top 50 predictions were validated by independent databases. These experimental results demonstrated that DF-MDA is a reliable and efficient method for predicting potential miRNA-disease associations.
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