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Frontiers of Computer Science

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Front. Comput. Sci.    2025, Vol. 19 Issue (4) : 194904    https://doi.org/10.1007/s11704-024-40060-2
Interdisciplinary
Computational approaches for circRNA-disease association prediction: a review
Mengting NIU1,2, Yaojia CHEN3,4, Chunyu WANG5, Quan ZOU3,4, Lei XU2()
1. Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
2. School of Applied Chemistry and Biological Technology, Shenzhen Polytechnic University, Shenzhen 518055, China
3. Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China,Chengdu 610054, China
4. Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China,Quzhou 324000, China
5. Faculty of Computing, Harbin Institute of Technology, Harbin 150006, China
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Abstract

Circular RNA (circRNA) is a covalently closed RNA molecule formed by back splicing. The role of circRNAs in posttranscriptional gene regulation provides new insights into several types of cancer and neurological diseases. CircRNAs are associated with multiple diseases and are emerging biomarkers in cancer diagnosis and treatment. The associations prediction is one of the current research hotspots in the field of bioinformatics. Although research on circRNAs has made great progress, the traditional biological method of verifying circRNA-disease associations is still a great challenge because it is a difficult task and requires much time. Fortunately, advances in computational methods have made considerable progress in circRNA research. This review comprehensively discussed the functions and databases related to circRNA, and then focused on summarizing the calculation model of related predictions, detailed the mainstream algorithm into 4 categories, and analyzed the advantages and limitations of the 4 categories. This not only helps researchers to have overall understanding of circRNA, but also helps researchers have a detailed understanding of the past algorithms, guide new research directions and research purposes to solve the shortcomings of previous research.
Keywords circular RNA      disease association prediction      machine learning      data mining      deep learning     
Corresponding Author(s): Lei XU   
Just Accepted Date: 24 April 2024   Issue Date: 20 June 2024
 Cite this article:   
Mengting NIU,Yaojia CHEN,Chunyu WANG, et al. Computational approaches for circRNA-disease association prediction: a review[J]. Front. Comput. Sci., 2025, 19(4): 194904.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-024-40060-2
https://academic.hep.com.cn/fcs/EN/Y2025/V19/I4/194904
Database circRNA Disease Association Description URL
CircR2Disease 661 100 725 It includs the circRNA and disease name, coordinates and gene symbols, expression mode. See bioinfo.snnu.edu.cn/CircR2Disease/ website.
circRNA disease 330 48 354 It includs circRNAs and diseases, including the circRNA ID and expression mode, disease, detection technology, circRNA biological function description. See cgga.org.cn:9091/circRNADisease/ website.
Circ2Disease 237 54 273 It includs circRNA and disease name, expression pattern, experimental method, and brief functional description of the CircrNa-disease relationship. See bioinformatics.zju.edu.cn/Circ2Disease/circRNAgroup.html website.
circMine 136,871 87 1,107 circMine offers 13 online analytical capabilities to assess the clinical and biological significance of circRNA. See biomedical-web.com/circmine/ website.
Circad 720 150 1,388 It lists validation methods, experimental validation states, primer information, and uses standardized nomenclature to standardize interpretations of associations. See clingen.igib.res.in/circad/ website.
CircR2Cancer 1,135 82 1,439 It includes information about cancer in the disease ontology as well as basic biological information about circRNAs. See biobdlab.cn:8000 website.
Tab.1  Databases for circRNA-disease associations
Fig.1  Classification and representation of computational methods for circRNA-disease prediction and grouping them based on an underlying computational model consisting of biological network, recommendation algorithms, machine learning, and deep learning. (a) Biological network-based methods; (b) recommender system-based methods; (c) machine learning-based methods; (d) deep learning-based methods
Fig.2  Computational models for predicting circRNA-disease associations
Calss Model Data Description AUC Code
Biological network-based methods KATZCPDA CircR2Disease KATZ 0.959,0.958 NA
NSL2CD CircR2Disease network embedding-based adaptive subspace learning 0.926 NA
PWCDA CircR2Disease computational path weighting model using the depth-first search algorithm 0.89 NA
NCPCDA CircR2Disease based on network consensus projection 0.884 See github.com/ghli16/NNCPCD website.
iCDA-CMG CircR2Disease, CircFunBase graph-based learning algorithm 0.862 NA
BRWSP CircR2Disease random walk 0.8675 NA
Recommender System-based method iCircDA-MF CircR2Disease matrix factorization 0.918 NA
RNMFLP CircR2Disease, CircRNADisease, Circ2Disease matrix factorization 0.9512 See github.com/biohnuster/RNMFLP website.
iCricDA-LTR CircFunBase learning to rank algorithm 0.928 See bliulab.net/iCircDA-LTR/ website.
IFCDA CircR2Disease CFR 0.946 NA
Machine learning-based methods iCDA-CGR CircR2Disease, circRNADisease, circFunBase, Circ2Disease support vector machine 0.8533 See github.com/look0012/iCDA-CGR website.
IMS-CDA CircR2Disease random forest classifier 0.8808 See github.com/look0012/IMS-CDA/ website.
DWNN-RLS CircR2Disease k-nearest neighbors 0.8854, 0.9205,0.9701 NA
XGBCDA CircR2Disease XGBoost 0.9860 See github.com/Q1DT/XGBCDA website.
NMFCDA CircR2Disease neural network Pseudoinverse Learning 0.9278 See github.com/look0012/NMFCDA website.
MSPCD CircFunBase neural networks 0.9904 See github.com/dayunliu/MSPCD website.
IGNSCDA CircR2Disease multilayer perceptron 0.829 NA
Deep learning-based methods RGCNCDA CircR2Disease map convolutional networks 0.8478 NA
MDGF-MCEC CircR2Disease map convolutional network 0.9744 See github.com/ABard0/MDGF-MCEC website.
DRGCNCDA circR2Cancer convolutional networks 0.9399 NA
SGANRDA CircR2Disease generative adversarial network 0.9411,0.9223 See github.com/look0012/SGANRDA/ website.
GANCDA CircR2Disease generation confrontation network 0.906 NA
GCNCDA CircR2Disease fastGCN 0.912,0.9278 See github.com/look0012/GCNCDA/ website.
GATCDA CircR2Diseas, CircAtlas2.0, Circ2Disease graph attention network 0.9011 NA
GraphCDA CircR2Disease graph convolutional network 0.9548 See github.com/Ziqiang-Liu/Predict website.
KGANCDA circR2Cancer knowledge graph attention network 0.8847 See github.com/lanbiolab/KGANCDA website.
GMNN2CD CircR2Disease graph convolutional network 0.9634 See github.com/nmt315320/GMNN2CD website.
iCircDA-NEAE CircR2Disease convolutional autoencoder 0.8962 See github.com/nathanyl/iCircDA-NEAE website.
CDHGNN CircR2Disease graph neural network model 0.886 NA
CLCDA CircR2Disease graph autoencoder 0.998 See github.com/Lxinmeng/CLCDA website.
Tab.2  Different computational methods to predict circRNA-disease associations
  
  
  
  
  
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