ArticleBMC biology2024
A computational model of circRNA-associated diseases based on a graph neural network: prediction and case studies for follow-up experimental validation.
Article in BMC biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 26 citations in OpenAlex.
- circGPAcorr: an integrative tool for functional annotation of circular RNAs using expression data.BioData mining · 2025Article
- AttenRNA: multi-scale deep attentive model with RNA feature variability analysis.Briefings in bioinformatics · 2025Article
- MVHGCN: Predicting circRNA-disease associations with multi-view heterogeneous graph convolutional neural networks.PLoS computational biology · 2025Article
- Interpretable multi-instance heterogeneous graph network learning modelling CircRNA-drug sensitivity association prediction.BMC biology · 2025Article
- iPiDA-LGE: a local and global graph ensemble learning framework for identifying piRNA-disease associations.BMC biology · 2025Article
- Circular RNA discovery with emerging sequencing and deep learning technologies.Nature genetics · 2025Review
- DGCLCMI: a deep graph collaboration learning method to predict circRNA-miRNA interactions.BMC biology · 2025Article
- GL4SDA: Predicting snoRNA-disease associations using GNNs and LLM embeddings.Computational and structural biotechnology journal · 2025Article
- Decoding circRNA translation: challenges and advances in computational method development.Frontiers in genetics · 2025Review
- Circular RNAs: novel noncoding players in male infertility.Hereditas · 2024Review
- CRBPSA: CircRNA-RBP interaction sites identification using sequence structural attention model.BMC biology · 2024Article
- iCRBP-LKHA: Large convolutional kernel and hybrid channel-spatial attention for identifying circRNA-RBP interaction sites.PLoS computational biology · 2024Article
- DiSMVC: a multi-view graph collaborative learning framework for measuring disease similarity.Bioinformatics (Oxford, England) · 2024Article
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Authors and funding
4 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundCircular RNAs (circRNAs) have been confirmed to play a vital role in the occurrence and development of diseases. Exploring the relationship between circRNAs and diseases is of far-reaching significance for studying etiopathogenesis and treating diseases. To this end, based on the graph Markov neural network algorithm (GMNN) constructed in our previous work GMNN2CD, we further considered the multisource biological data that affects the association between circRNA and disease and developed an updated web server CircDA and based on the human hepatocellular carcinoma (HCC) tissue data to verify the prediction results of CircDA.
resultsCircDA is built on a Tumarkov-based deep learning framework. The algorithm regards biomolecules as nodes and the interactions between molecules as edges, reasonably abstracts multiomics data, and models them as a heterogeneous biomolecular association network, which can reflect the complex relationship between different biomolecules. Case studies using literature data from HCC, cervical, and gastric cancers demonstrate that the CircDA predictor can identify missing associations between known circRNAs and diseases, and using the quantitative real-time PCR (RT-qPCR) experiment of HCC in human tissue samples, it was found that five circRNAs were significantly differentially expressed, which proved that CircDA can predict diseases related to new circRNAs.
conclusionsThis efficient computational prediction and case analysis with sufficient feedback allows us to identify circRNA-associated diseases and disease-associated circRNAs. Our work provides a method to predict circRNA-associated diseases and can provide guidance for the association of diseases with certain circRNAs. For ease of use, an online prediction server ( http://server.malab.cn/CircDA ) is provided, and the code is open-sourced ( https://github.com/nmt315320/CircDA.git ) for the convenience of algorithm improvement.
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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.