ArticleMolecular therapy. Nucleic acids2024
Joint masking and self-supervised strategies for inferring small molecule-miRNA associations.
Article in Molecular therapy. Nucleic acids, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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35 citing papers in PubMed.
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- Transcriptome Analysis of Differentially Expressed Genes and Molecular Pathways Involved During Osteoclast Differentiation.Molecular biotechnology · 2026Article
- Therapeutic modulation of HIV-1 using miRNAs and lncRNAs: from bench to bedside.Virology journal · 2026Review
- PRGNet: a Parallel Residual Graph Network for enhanced drug-target binding affinity prediction.BMC genomics · 2026Article
- Differentiation of RNA-protein docking structures through molecular dynamics simulation and machine learning methods.Briefings in bioinformatics · 2026Article
- Long non-coding RNA HOXC-AS3 promotes proliferation and metastasis of esophageal squamous carcinoma by targeting WNT3A through binding to YBX1.BMC cancer · 2026Article
- The role of PLOD family genes in liver hepatocellular carcinoma: from mechanisms to therapeutic potential.BMC cancer · 2026Article
- Plasma miR-134-5p: a candidate biomarker for predicting non-response to anti-TNF therapy in rheumatoid arthritis.Frontiers in immunology · 2026Observational
- Epigenetic regulation of PANoptosis: DNA methylation, histone modifications and non-coding RNAs.EXCLI journal · 2026Review
- Article
- iDNA-DAPHA: a generic framework for methylation prediction via domain-adaptive pretraining and hierarchical attention.Briefings in bioinformatics · 2025Article
- ESAE-SDA: ensemble sparse autoencoder framework for epigenomics-informed snoRNA-disease associations prediction.BMC bioinformatics · 2025Article
- Machine learning approaches for predicting the small molecule-miRNA associations: a comprehensive review.Molecular diversity · 2025Review
- LDA-SCGB: inferring lncRNA-disease associations based on condensed gradient boosting.BMC bioinformatics · 2025Article
- Graph-RPI: predicting RNA-protein interactions via graph autoencoder and self-supervised learning strategies.Briefings in bioinformatics · 2025Article
- Geometry-enhanced graph neural networks accelerate circRNA therapeutic target discovery.Frontiers in genetics · 2025Article
- The epitranscriptome meets non-coding RNA: m6A-mediated regulation in oncogenesis and therapy.Frontiers in cell and developmental biology · 2025Review
- Deciphering metabolic disease mechanisms for natural medicine discovery via graph autoencoders.Frontiers in pharmacology · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inferring small molecule-miRNA associations (MMAs) is crucial for revealing the intricacies of biological processes and disease mechanisms. Deep learning, renowned for its exceptional speed and accuracy, is extensively used for predicting MMAs. However, given their heavy reliance on data, inaccuracies during data collection can make these methods susceptible to noise interference. To address this challenge, we introduce the joint masking and self-supervised (JMSS)-MMA model. This model synergizes graph autoencoders with a probability distribution-based masking strategy, effectively countering the impact of noisy data and enabling precise predictions of unknown MMAs. Operating in a self-supervised manner, it deeply encodes the relationship data of small molecules and miRNA through the graph autoencoder, delving into its latent information. Our masking strategy has successfully reduced data noise, enhancing prediction accuracy. To our knowledge, this is the pioneering integration of a masking strategy with graph autoencoders for MMA prediction. Furthermore, the JMSS-MMA model incorporates a node-degree-based decoder, deepening the understanding of the network's structure. Experiments on two mainstream datasets confirm the model's efficiency and precision, and ablation studies further attest to its robustness. We firmly believe that this model will revolutionize drug development, personalized medicine, and biomedical research.
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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 OpenQuestion graph.