Circular RNAs play a crucial role in cell development and serve as biomarkers in many diseases. Nevertheless, the function of many circular RNAs remains unknown. This function can be inferred from sponging and silencing interactions with micro RNAs and messenger RNAs. We recently proposed a network-based circRNA functional annotation tool, circGPA. However, validation data for RNA interactions are often sparse and predicted interactions contain many false positives. To address this issue, we propose an extended algorithm named circGPAcorr, which uses expression data to weight the interactions, resulting in more precise functional annotation. To assess the significance of the results, the p-value is calculated using reduction to circGPA, a generating-polynomial-based method. We show that the problem is #P-hard, and thus computationally difficult. The circGPAcorr algorithm is tested on publicly available myelodysplastic syndromes expression data, providing gene ontology annotations that align with the literature on myelodysplastic syndromes. At the same time, we demonstrate its performance in the circRNA-disease annotation task.

Department of Computer Science Faculty of Electrical Engineering Czech Technical University Prague Technická 16627 Prague Prague Czech Republic

Department of Genomics Institute of Hematology and Blood Transfusion U Nemocnice 12800 Prague Prague Czech Republic

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Gomes AQ, Nolasco S, Soares H. Non-coding RNAs: multi-tasking molecules in the cell. Int J Mol Sci. 2013;14(8):16010–39. PubMed PMC

Oliver S. Guilt-by-association goes global. Nature. 2000;403(6770):601–2. PubMed

Ryšavý P, Kléma J, Merkerová MD. circGPA: circRNA functional annotation based on probability-generating functions. BMC Bioinformatics. 2022;23(1):392. PubMed PMC

Feller W. An introduction to probability theory and its applications, vol. 81. USA: John Wiley & Sons; 1991.

Ryšavý P, Kléma J, Merkerová MD. GPACDA - circRNA-Disease Association Prediction with Generating Polynomials. In: Rojas I, Ortuño F, Rojas F, Herrera LJ, Valenzuela O, editors. Bioinformatics and Biomedical Engineering. Cham: Springer Nature Switzerland; 2024. p. 33–48.

Skoufos G, Kakoulidis P, Tastsoglou S, Zacharopoulou E, Kotsira V, Miliotis M, et al. TarBase-v9. 0 extends experimentally supported miRNA–gene interactions to cell-types and virally encoded miRNAs. Nucleic Acids Res. 2024;52(D1):D304–10. PubMed PMC

Chi SW, Zang JB, Mele A, Darnell RB. Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature. 2009;460(7254):479–86. PubMed PMC

Hafner M, Landthaler M, Burger L, Khorshid M, Hausser J, Berninger P, et al. Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell. 2010;141(1):129–41. PubMed PMC

Haecker I, Renne R. HITS-CLIP and PAR-CLIP advance viral miRNA targetome analysis. Crit Rev Eukaryot Gene Expr. 2014;24(2):101–16. 10.1615/critreveukaryotgeneexpr.2014006367. PubMed PMC

Agarwal V, Bell GW, Nam JW, Bartel DP. Predicting effective microrna target sites in mammalian mrnas. eLife. 2015;4: e05005. 10.7554/eLife.05005. PubMed PMC

Leclercq M, Diallo AB, Blanchette M. Prediction of human miRNA target genes using computationally reconstructed ancestral mammalian sequences. Nucleic Acids Res. 2017;45(2):556–66. PubMed PMC

Pinzón N, Li B, Martinez L, Sergeeva A, Presumey J, Apparailly F, et al. Microrna target prediction programs predict many false positives. Genome Res. 2017;27(2):234–45. PubMed PMC

Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:1–13. PubMed PMC

Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005;4(1). https://www.degruyterbrill.com/document/doi/10.2202/1544-6115.1128/html. PubMed DOI

Buratin A, Gaffo E, Dal Molin A, Bortoluzzi S. CircIMPACT: an R package to explore circular RNA impact on gene expression and pathways. Genes. 2021;12(7):1044. PubMed PMC

Burns JJ, Shealy BT, Greer MS, Hadish JA, McGowan MT, Biggs T, et al. Addressing noise in co-expression network construction. Brief Bioinform. 2022;23(1): bbab495. PubMed PMC

Cardenas J, Balaji U, Gu J. Cerina: systematic circRNA functional annotation based on integrative analysis of ceRNA interactions. Sci Rep. 2020;10(1): 22165. PubMed PMC

Shi Z, Wang J, Zhang B. NetGestalt: integrating multidimensional omics data over biological networks. Nat Methods. 2013;10(7):597–8. PubMed PMC

Deng L, Lin W, Wang J, Zhang J. DeepciRGO: functional prediction of circular RNAs through hierarchical deep neural networks using heterogeneous network features. BMC Bioinformatics. 2020;21:1–18. PubMed PMC

Chen Y, Wang J, Wang C, Liu M, Zou Q. Deep learning models for disease-associated circRNA prediction: a review. Brief Bioinform. 2022;23(6): bbac364. PubMed

Lasantha D, Vidanagamachchi S, Nallaperuma S. Deep learning and ensemble deep learning for circRNA-RBP interaction prediction in the last decade: a review. Eng Appl Artif Intell. 2023;123:106352.

Li G, Yue Y, Liang C, Xiao Q, Ding P, Luo J. NCPCDA: network consistency projection for circRNA-disease association prediction. RSC Adv. 2019;9(57):33222–8. 10.1039/C9RA06133A. PubMed PMC

Yao B, and YS. lncRNA-disease association prediction based on optimizing measures of multi-graph regularized matrix factorization. Comput Methods Biomech Biomed Eng. 2025;0(0):1–16. PMID: 40114384. 10.1080/10255842.2025.2479854. PubMed

Zhao Q, Yang Y, Ren G, Ge E, Fan C. Integrating bipartite network projection and KATZ measure to identify novel circRNA-disease associations. IEEE Trans Nanobioscience. 2019;18(4):578–84. 10.1109/TNB.2019.2922214. PubMed

Lu C, Zeng M, Wu FX, Li M, Wang J. Improving circRNA–disease association prediction by sequence and ontology representations with convolutional and recurrent neural networks. Bioinformatics. 2020;36(24):5656–64. 10.1093/bioinformatics/btaa1077. PubMed

Zhang HY, Wang L, You ZH, Hu L, Zhao BW, Li ZW, et al. iGRLCDA: identifying circRNA–disease association based on graph representation learning. Brief Bioinform. 2022;23(3): bbac083. 10.1093/bib/bbac083. PubMed

Deepthi K, Jereesh AS. An ensemble approach for CircRNA-disease association prediction based on autoencoder and deep neural network. Gene. 2020;762:145040. 10.1016/j.gene.2020.145040. PubMed

Wu Q, Deng Z, Pan X, Shen HB, Choi KS, Wang S, et al. MDGF-MCEC: a multi-view dual attention embedding model with cooperative ensemble learning for circRNA-disease association prediction. Brief Bioinform. 2022;23(5): bbac289. 10.1093/bib/bbac289. PubMed

Guo Y, Yi M. THGNCDA: circRNA–disease association prediction based on triple heterogeneous graph network. Brief Funct Genom. 2023:elad042. 10.1093/bfgp/elad042. PubMed

Liang S, Liu S, Song J, Lin Q, Zhao S, Li S, et al. HMCDA: a novel method based on the heterogeneous graph neural network and metapath for circRNA-disease associations prediction. BMC Bioinformatics. 2023;24(1): 335. 10.1186/s12859-023-05441-7. PubMed PMC

Niu M, Wang C, Zhang Z, Zou Q. A computational model of circRNA-associated diseases based on a graph neural network: prediction and case studies for follow-up experimental validation. BMC Biol. 2024;22(1):24. PubMed PMC

Lan W, Li C, Chen Q, Yu N, Pan Y, Zheng Y, et al. LGCDA: Predicting CircRNA-Disease Association Based on Fusion of Local and Global Features. IEEE/ACM Trans Comput Biol Bioinforma. 2024;21(5):1413–22. 10.1109/TCBB.2024.3387913. PubMed

Warden CD, Yuan YC, Wu X. Optimal calculation of RNA-Seq fold-change values. Int J Comput Bioinforma In Silico Model. 2013;2(6):285–92.

Dudekula DB, Panda AC, 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. PubMed PMC

Trsova I, et al. Expression of circular RNAs in myelodysplastic neoplasms and their association with mutations in the splicing factor gene SF3B1. Mol Oncol. 2023;17(12):2565–83. 10.1002/1878-0261.13486. PubMed PMC

Hrustincova A, Krejcik Z, Kundrat D, Szikszai K, Belickova M, Pecherkova P, et al. Circulating small noncoding RNAs have specific expression patterns in plasma and extracellular vesicles in myelodysplastic syndromes and are predictive of patient outcome. Cells. 2020. 10.3390/cells9040794. PubMed PMC

Merkerová MD, Kléma J, Kundrát D, Szikszai K, Krejčík Z, Hruštincová A, et al. Noncoding RNAs and Their Response Predictive Value in Azacitidine-treated Patients With Myelodysplastic Syndrome and Acute Myeloid Leukemia With Myelodysplasia-related Changes. Cancer Genomics Proteomics. 2022;19(2):205–228. 10.21873/cgp.20315. PubMed PMC

Dunn OJ. Multiple Comparisons among Means. JASA. 1961;56(293)52–64.

Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. JRSS Ser B. 1995;57(1)289–300.

Merico D, Isserlin R, Stueker O, Emili A, Bader GD. Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation. PLoS ONE. 2010;5(11):1–12. 10.1371/journal.pone.0013984. PubMed PMC

Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504. 10.1101/gr.1239303. PubMed PMC

Marriott FHC. Barnard’s Monte Carlo Tests: How Many Simulations? J R Stat Soc Ser C Appl Stat. 1979;28(1):75–7. http://www.jstor.org/stable/2346816.

Zhang Z, Yang T, Xiao J. Circular RNAs: promising biomarkers for human diseases. eBioMedicine. 2018;34:267–74. 10.1016/j.ebiom.2018.07.036. PubMed PMC

Haferlach T, Nagata Y, Grossmann V, Okuno Y, Bacher U, Nagae G, et al. Landscape of genetic lesions in 944 patients with myelodysplastic syndromes. Leukemia. 2014;28(2):241–7. 10.1038/leu.2013.336. PubMed PMC

Davis AP, Wiegers TC, Johnson RJ, Sciaky D, Wiegers J, Mattingly C. Comparative Toxicogenomics Database (CTD): update 2023. Nucleic Acids Res. 2022;51(D1):D1257–62. 10.1093/nar/gkac833. PubMed PMC

Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. Nat Genet. 2000;25(1):25–9. 10.1038/75556. PubMed PMC

Yao D, Zhang L, Zheng M, Sun X, Lu Y, Liu P. Circ2disease: a manually curated database of experimentally validated circRNAs in human disease. Sci Rep. 2018. 10.1038/s41598-018-29360-3. PubMed PMC

Fan C, Lei X, Tie J, Zhang Y, Wu FX, Pan Y. Circr2disease v2.0: an updated web server for experimentally validated circRNA–disease associations and its application. Genomics Proteomics Bioinforma. 2021;20(3):435–45. 10.1016/j.gpb.2021.10.002. PubMed PMC

Sun ZY, Yang CL, Huang LJ, Mo ZC, Zhang KN, Fan WH, et al. circRNADisease v2.0: an updated resource for high-quality experimentally supported circRNA-disease associations. Nucleic Acids Res. 2023;52(D1):D1193–200. 10.1093/nar/gkad949. PubMed PMC

Edgar R. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30(1):207–10. 10.1093/nar/30.1.207. PubMed PMC

Liu S, Wang Y, Duan L, Cui D, Deng K, Dong Z, et al. Whole transcriptome sequencing identifies a competitive endogenous RNA network that regulates the immunity of bladder cancer. Heliyon. 2024;10(8):e29344. 10.1016/j.heliyon.2024.e29344. PubMed PMC

Lorenzi L, Chiu HS, Avila Cobos F, Gross S, Volders PJ, Cannoodt R, et al. The RNA atlas expands the catalog of human non-coding RNAs. Nat Biotechnol. 2021;39(11):1453–65. 10.1038/s41587-021-00936-1. PubMed

Glažar P, Papavasileiou P, Rajewsky N. Circbase: a database for circular RNAs. RNA. 2014;20(11):1666–70. 10.1261/rna.043687.113. PubMed PMC

Bekker J, Davis J. Learning from positive and unlabeled data: a survey. Mach Learn. 2020;109(4):719–60. 10.1007/s10994-020-05877-5.

Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS. A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst. 2021;32(1):4–24. 10.1109/TNNLS.2020.2978386. PubMed

Šourek G, Aschenbrenner V, Železný F, Schockaert S, Kuželka O. Lifted relational neural networks: efficient learning of latent relational structures. J Artif Intell Res. 2018;62:69–100.