ReviewBriefings in bioinformatics2021
Circular RNAs and complex diseases: from experimental results to computational models.
Review in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers.
What it found
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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.
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Who cites it
76 citing papers in PubMed.
- Improving hERG Cardiotoxicity Prediction via SMILES Transformer, Molecular Fingerprints, and Layer-Wise Self-Adaptive Graph Attention Network.ACS omega · 2026Article
- Circular RNAs: An Under-Recognized Part in Different Scenarios of Host-Parasite Interrelationships.Pathogens (Basel, Switzerland) · 2026Review
- Bioinformatics and machine learning integration reveals a novel 4-gene (GFUS, ARHGAP8, NBL1, and ACTB) biomarker model for prostate cancer.Discover oncology · 2026Article
- DAMFCMI: capturing cross-view interactions via hybrid attention for circRNA-MiRNA interaction prediction.Bioinformatics advances · 2026Article
- Circular RNA circSTX12 regulates osteo-adipogenic balance and proliferation of BMSCs in senile osteoporosis.Cellular and molecular life sciences : CMLS · 2025Article
- Predicting circRNA-disease associations with shared units and multi-channel attention mechanisms.Bioinformatics (Oxford, England) · 2025Article
- Prediction of circRNA-Disease Associations via Graph Isomorphism Transformer and Dual-Stream Neural Predictor.Biomolecules · 2025Article
- Multi-task aquatic toxicity prediction model based on multi-level features fusion.Journal of advanced research · 2025Article
- Modeling ncRNA Synergistic Regulation in Cancer.Methods in molecular biology (Clifton, N.J.) · 2025Review
- The improved de Bruijn graph for multitask learning: predicting functions, subcellular localization, and interactions of noncoding RNAs.Briefings in bioinformatics · 2024Article
- LncRNA-miRNA interactions prediction based on meta-path similarity and Gaussian kernel similarity.Journal of cellular and molecular medicine · 2024Article
- Multi-view learning framework for predicting unknown types of cancer markers via directed graph neural networks fitting regulatory networks.Briefings in bioinformatics · 2024Article
- MRDPDA: A multi-Laplacian regularized deepFM model for predicting piRNA-disease associations.Journal of cellular and molecular medicine · 2024Article
- Exploring potential circRNA biomarkers for cancers based on double-line heterogeneous graph representation learning.BMC medical informatics and decision making · 2024Article
- MiRAGE: mining relationships for advanced generative evaluation in drug repositioning.Briefings in bioinformatics · 2024Article
- MiRAGE: mining relationships for advanced generative evaluation in drug repositioning.Briefings in bioinformatics · 2024Article
- Identification of circRNA-disease associations via multi-model fusion and ensemble learning.Journal of cellular and molecular medicine · 2024Review
- Review
- CircSI-SSL: circRNA-binding site identification based on self-supervised learning.Bioinformatics (Oxford, England) · 2024Article
- MLFLHMDA: predicting human microbe-disease association based on multi-view latent feature learning.Frontiers in microbiology · 2024Article
16 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Circular RNAs (circRNAs) are a class of single-stranded, covalently closed RNA molecules with a variety of biological functions. Studies have shown that circRNAs are involved in a variety of biological processes and play an important role in the development of various complex diseases, so the identification of circRNA-disease associations would contribute to the diagnosis and treatment of diseases. In this review, we summarize the discovery, classifications and functions of circRNAs and introduce four important diseases associated with circRNAs. Then, we list some significant and publicly accessible databases containing comprehensive annotation resources of circRNAs and experimentally validated circRNA-disease associations. Next, we introduce some state-of-the-art computational models for predicting novel circRNA-disease associations and divide them into two categories, namely network algorithm-based and machine learning-based models. Subsequently, several evaluation methods of prediction performance of these computational models are summarized. Finally, we analyze the advantages and disadvantages of different types of computational models and provide some suggestions to promote the development of circRNA-disease association identification from the perspective of the construction of new computational models and the accumulation of circRNA-related data.
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What Socratic 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 Socratic graph.