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Abstract

Introduction

CircRNA, with its covalently closed circular structure, plays key roles in biological functions and diseases by interacting with RNA-binding proteins (RBPs) and microRNAs (miRNAs). However, existing computational methods struggle to capture secondary structure features.

Methods

We introduce CSGN, a graph neural network model that predicts circRNA-RBP interactions using secondary structure information. CSGN enhances physicochemical feature encodings by incorporating pseudo-secondary structures from thermodynamic models and utilizes graph convolutional networks (GCNs) for feature extraction. It also integrates Doc2Vec embeddings and employs CNNs, BiGRUs, and MLPs for efficient feature representation.

Results

CSGN outperforms existing models across 16 datasets. Ablation studies confirm the significance of RNA secondary structure and GCNs in improving prediction accuracy. Principal component analysis further highlights CSGN's strength in feature extraction.

Discussion

CSGN advances circRNA-RBP prediction by integrating GCNs and Doc2Vec, though global structural constraints remain. Future work should address longer-sequence modeling and experimental validation.

Conclusion

CSGN effectively improves circRNA-RBP interaction prediction, demonstrating superior performance through the integration of RNA secondary structure and GCNs.

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2025-10-21
2026-02-04

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References

  1. Du W.W. Zhang C. Yang W. Yong T. Awan F.M. Yang B.B. Identifying and characterizing circRNA-protein interaction. Theranostics 2017 7 17 4183 4191 10.7150/thno.21299 29158818
    [Google Scholar]
  2. Caiment F. Gaj S. Claessen S. Kleinjans J. High-throughput data integration of RNA–miRNA–circRNA reveals novel insights into mechanisms of benzo[a]pyrene-induced carcinogenicity. Nucleic Acids Res. 2015 43 5 2525 2534 10.1093/nar/gkv115 25690898
    [Google Scholar]
  3. Lukiw W.J. Circular RNA (circRNA) in Alzheimer’s disease (AD). Front. Genet. 2013 4 307 10.3389/fgene.2013.00307 24427167
    [Google Scholar]
  4. Hansen T.B. Jensen T.I. Clausen B.H. Natural RNA circles function as efficient microRNA sponges. Nature 2013 495 7441 384 388 10.1038/nature11993 23446346
    [Google Scholar]
  5. Memczak S. Jens M. Elefsinioti A. Circular RNAs are a large class of animal RNAs with regulatory potency. Nature 2013 495 7441 333 338 10.1038/nature11928 23446348
    [Google Scholar]
  6. Hentze M.W. Preiss T. Circular RNAs: Splicing’s enigma variations. EMBO J. 2013 32 7 923 925 10.1038/emboj.2013.53 23463100
    [Google Scholar]
  7. Yang Y. Fan X. Mao M. Extensive translation of circular RNAs driven by N 6 -methyladenosine. Cell Res. 2017 27 5 626 641 10.1038/cr.2017.31 28281539
    [Google Scholar]
  8. Pamudurti N.R. Bartok O. Jens M. Translation of CircRNAs. Mol. Cell 2017 66 1 9 21.e7 10.1016/j.molcel.2017.02.021 28344080
    [Google Scholar]
  9. Legnini I. Di Timoteo G. Rossi F. Circ-ZNF609 is a circular rna that can be translated and functions in myogenesis. Mol. Cell 2017 66 1 22 37.e9 10.1016/j.molcel.2017.02.017 28344082
    [Google Scholar]
  10. Gagliardi M. Matarazzo M.R. RIP: RNA immunoprecipitation. In: Polycomb Group Proteins: Methods and Protocols. NY Humana Press 2016 73 86 10.1007/978‑1‑4939‑6380‑5_7
    [Google Scholar]
  11. Barnes C. Kanhere A. Identification of RNA–protein interactions through in vitro RNA pull-down assays. In: Polycomb Group Proteins: Methods and Protocols. NY Springer 2016 99 113 10.1007/978‑1‑4939‑6380‑5_9
    [Google Scholar]
  12. Li B. Zhang X-Q. Liu S-R. Discovering the interactions between circular RNAs and RNA-binding proteins from CLIP-seq data using circScan. bioRxiv 2017 115980 10.1101/115980
    [Google Scholar]
  13. Wen S. Liu Y. Yang G. Author correction: A method for miRNA-disease association prediction using machine learning decoding of multi-layer heterogeneous graph transformer encoded representations. Sci. Rep. 2024 14 1 24181 10.1038/s41598‑024‑76003‑x 39406809
    [Google Scholar]
  14. Yang J. Lei X. Zhang F. Identification of circRNA ‐disease associations via multi‐model fusion and ensemble learning. J. Cell. Mol. Med. 2024 28 7 e18180 10.1111/jcmm.18180 38506066
    [Google Scholar]
  15. Hu H. Feng Z. Lin H. Gene function and cell surface protein association analysis based on single-cell multiomics data. Comput. Biol. Med. 2023 157 106733 10.1016/j.compbiomed.2023.106733 36924730
    [Google Scholar]
  16. Meng R. Yin S. Sun J. Hu H. Zhao Q. scAAGA: Single cell data analysis framework using asymmetric autoencoder with gene attention. Comput. Biol. Med. 2023 165 107414 10.1016/j.compbiomed.2023.107414 37660567
    [Google Scholar]
  17. Wang T. Sun J. Zhao Q. Investigating cardiotoxicity related with hERG channel blockers using molecular fingerprints and graph attention mechanism. Comput. Biol. Med. 2023 153 106464 10.1016/j.compbiomed.2022.106464 36584603
    [Google Scholar]
  18. Chen Z. Zhang L. Sun J. Meng R. Yin S. Zhao Q. DCAMCP: A deep learning model based on capsule network and attention mechanism for molecular carcinogenicity prediction. J. Cell. Mol. Med. 2023 27 20 3117 3126 10.1111/jcmm.17889 37525507
    [Google Scholar]
  19. Zhang K. Pan X. Yang Y. Shen H.B. CRIP: Predicting circRNA–RBP-binding sites using a codon-based encoding and hybrid deep neural networks. RNA 2019 25 12 1604 1615 10.1261/rna.070565.119 31537716
    [Google Scholar]
  20. Wang Z. Lei X. Wu F.X. Identifying cancer-specific circRNA–RBP binding sites based on deep learning. Molecules 2019 24 22 4035 10.3390/molecules24224035 31703384
    [Google Scholar]
  21. Liu L. Wei Y. Tan Z. Zhang Q. Sun J. Zhao Q. Predicting circRNA-RBP binding sites using a hybrid deep neural network. Interdiscip. Sci. 2024 16 3 635 648 10.1007/s12539‑024‑00616‑z 38381315
    [Google Scholar]
  22. Yang Y. Hou Z. Ma Z. Li X. Wong K.C. iCircRBP-DHN: Identification of circRNA-RBP interaction sites using deep hierarchical network. Brief. Bioinform. 2021 22 4 bbaa274 10.1093/bib/bbaa274 33126261
    [Google Scholar]
  23. Li H. Deng Z. Yang H. circRNA-binding protein site prediction based on multi-view deep learning, subspace learning and multi-view classifier. Brief. Bioinform. 2022 23 1 bbab394 10.1093/bib/bbab394 34571539
    [Google Scholar]
  24. Sun L. Xu K. Huang W. Predicting dynamic cellular protein–RNA interactions by deep learning using in vivo RNA structures. Cell Res. 2021 31 5 495 516 10.1038/s41422‑021‑00476‑y 33623109
    [Google Scholar]
  25. Lorenz R. Bernhart S.H. Siederdissen C.H.Z. ViennaRNA package 2.0. Algorithms Mol. Biol. 2011 6 26 10.1186/1748‑7188‑6‑26 22115189
    [Google Scholar]
  26. Dudekula D.B. Panda A.C. Grammatikakis I. De S. Abdelmohsen K. Gorospe M. CircInteractome: A web tool for exploring circular RNAs and their interacting proteins and microRNAs. RNA Biol. 2016 13 1 34 42 10.1080/15476286.2015.1128065 26669964
    [Google Scholar]
  27. Pan X. Shen H.B. RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach. BMC Bioinformatics 2017 18 1 136 10.1186/s12859‑017‑1561‑8 28245811
    [Google Scholar]
  28. Li W. Godzik A. Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 2006 22 13 1658 1659 10.1093/bioinformatics/btl158 16731699
    [Google Scholar]
  29. Quan Z.O.U. Mao-zu G.U.O. Tao-tao Z. A Review of RNA Secondary Structure Prediction Algorithms. Tien Tzu Hsueh Pao 2008 36 2 331 337
    [Google Scholar]
  30. Bari A.T.M.G. Reaz M.R. Choi H-J. Jeong B-S. DNA encoding for splice site prediction in large DNA sequence. Berlin, Heidelberg Springer Berlin Heidelberg 2013 46 58 10.1007/978‑3‑642‑40270‑8_4
    [Google Scholar]
  31. Manavalan B. Basith S. Shin T.H. Wei L. Lee G. Meta-4mCpred: A sequence-based meta-predictor for accurate DNA 4mC site prediction using effective feature representation. Mol. Ther. Nucleic Acids 2019 16 733 744 10.1016/j.omtn.2019.04.019 31146255
    [Google Scholar]
  32. Liu K. Chen W. iMRM: A platform for simultaneously identifying multiple kinds of RNA modifications. Bioinformatics 2020 36 11 3336 3342 10.1093/bioinformatics/btaa155 32134472
    [Google Scholar]
  33. Kipf T.N. Welling M. Semi-supervised classification with graph convolutional networks. arXiv 2016 arXiv:1609.02907v4 10.48550/arXiv.1609.02907
    [Google Scholar]
  34. Chung J. Gulcehre C. Cho K. Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv 2014 arXiv:1412.3555v1 10.48550/arXiv.1412.3555
    [Google Scholar]
  35. Varma S. Simon R. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics 2006 7 1 91 10.1186/1471‑2105‑7‑91 16504092
    [Google Scholar]
  36. Bradley A.P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit. 1997 30 7 1145 1159 10.1016/S0031‑3203(96)00142‑2
    [Google Scholar]
  37. Jia C. Bi Y. Chen J. Leier A. Li F. Song J. PASSION: an ensemble neural network approach for identifying the binding sites of RBPs on circRNAs. Bioinformatics 2020 36 15 4276 4282 10.1093/bioinformatics/btaa522 32426818
    [Google Scholar]
  38. Greenacre M. Groenen P.J.F. Hastie T. D’Enza A.I. Markos A. Tuzhilina E. Principal component analysis. Nat Rev Methods Primers 2022 2 1 100 10.1038/s43586‑022‑00184‑w
    [Google Scholar]
/content/journals/cbio/10.2174/0115748936393849250830080429
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A Hybrid Deep Neural Network Utilizing Graph Convolution for the Prediction of CircRNA-RBP Interaction
Bentham Science Publishers ; https://doi.org/10.2174/0115748936393849250830080429
/content/journals/cbio/10.2174/0115748936393849250830080429
/content/journals/cbio/10.2174/0115748936393849250830080429
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  • Article Type:     Research Article
Keywords: graph convolution ; Graph convolutional networks (GCNs) ; circRNA-RBP interaction ; bidirectional gated recurrent units ; convolutional neural networks ; secondary structure

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