Prediction of piRNA and Disease Association based on Graph Neural Network
DOI:
https://doi.org/10.54097/n30nxy05Keywords:
piRNA Disease Association, Graph Neural Network, TransformerAbstract
Piwi interacting RNA (piRNA) is a type of small non coding RNA with a length of 24-32 nucleotides, mainly expressed in germ cells. Its abnormal expression is closely related to various diseases such as cancer and neurodegenerative diseases. Although biological experiments are the gold standard for identifying the association between piRNA and disease (PDA), their high cost and long cycle limit research progress. Therefore, computational models have become an important tool for assisting in predicting PDA. However, existing computational methods generally suffer from issues such as insufficient feature extraction and imbalanced data. This article proposes a prediction model based on the fusion of graph convolutional network and attention mechanism - RandGCN. This model combines piRNA sequence embedding, heterogeneous graph construction based on random walks, multi-layer graph convolution feature extraction, and multi head attention mechanism with gating units, effectively improving the accuracy and robustness of PDA prediction. The experimental results on the MNDR dataset show that RandGCN performs well in both AUC and AUPR values, demonstrating excellent predictive performance and potential applications.
Downloads
References
[1] Aravin A, Gaidatzis D, Pfeffer S, et al. A novel class of small RNAs bind to MILI protein in mouse testes. Nature 2006; 442 (7099): 203–7. DOI: https://doi.org/10.1038/nature04916
[2] Liu Y, Dou M, Song X, et al. The emerging role of the piRNA/piwi complex in cancer. Mol Cancer 2019;18(1):123. DOI: https://doi.org/10.1186/s12943-019-1052-9
[3] Iwasaki YW, Siomi MC, Siomi H. PIWI-interacting RNA: its biogenesis and functions. Annu Rev Biochem 2015;84(1):405–33. DOI: https://doi.org/10.1146/annurev-biochem-060614-034258
[4] Aravin AA, Hannon GJ, Brennecke J. The piwi-piRNA pathway provides an adaptive defense in the transposon arms race. Science 2007;318(5851):761–4. DOI: https://doi.org/10.1126/science.1146484
[5] Seto AG, Kingston RE,Lau NC. The coming of age for piwi proteins. Mol Cell 2007;26(5):603–9. DOI: https://doi.org/10.1016/j.molcel.2007.05.021
[6] Wang K,Wang T,Gao XQ, Chen XZ, Wang F, Zhou LY. Emerging functions of piwi-interacting RNAs in diseases. Journal of Cellular and Molecular Medicine.2021; 25(11): 4893–901. DOI: https://doi.org/10.1111/jcmm.16466
[7] Zhou JY, Zhou WY, Zhang R. The potential mechanisms of piRNA to induce hepatocellular carcinoma in human. Med Hypotheses.2021;146:110400. DOI: https://doi.org/10.1016/j.mehy.2020.110400
[8] Xie Q, Li Z, Luo X, et al. piRNA-14633 promotes cervical cancer cell malig‑nancy in a METTL14-dependent m6A RNA methylation manner. J Transl Med. 2022;20(1):51. DOI: https://doi.org/10.1186/s12967-022-03257-2
[9] Zhong N, Nong XT, Diao JY, et al. piRNA-6426 increases DNMT3Bmediated SOAT1 methylation and improves heart failure. Aging-Us. 2022; 14:2678–94. DOI: https://doi.org/10.18632/aging.203965
[10] Ernst C, Odom DT,Kutter C. The emergence of piRNAs against transposon invasion to preserve mammalian genome integrity. Nat Commun 2017;8(1):10. DOI: https://doi.org/10.1038/s41467-017-01049-7
[11] Thakker DR, Natt F, Hüsken D, Maier R, Müller M, van der Putten H, et al. Neurochemical and behavioral consequences of widespread gene knockdown in the adult mouse brain by using nonviral RNA interference. Proc Natl Acad Sci USA 2004; 101: 17270–5. DOI: https://doi.org/10.1073/pnas.0406214101
[12] Chen X, Sun Y-Z,Guan N-N, Qu J, Huang Z-A,Zhu Z-X, et al. Computational models for lncRNA function prediction and functional similarity calculation.Brief Funct Genom 2019;18: 58–82. DOI: https://doi.org/10.1093/bfgp/ely031
[13] Bagci H, Sriskandarajah N, Robert A, et al. Ribosomes guide pachytene piRNA formation on long intergenic piRNA precursors. Nat Cell Biol. 2020; 22:353-353. DOI: https://doi.org/10.1038/s41556-020-0482-3
[14] Wei H, Xu Y, Liu B. iPiDi-PUL:identifying Piwi-interacting RNA-disease associations based on positive unlabeled learning. Brief Bioinforma 2021;22: bbaa058. DOI: https://doi.org/10.1093/bib/bbaa058
[15] You Z-H, Huang Z-A, Zhu Z, Yan G-Y, Li Z-W, Wen Z, et al. PBMDA: a novel and effective path-based computational model for miRNA-disease association prediction. PLoS Comput Biol 2017;13: e1005455. DOI: https://doi.org/10.1371/journal.pcbi.1005455
[16] Chen X, Xie D, Zhao Q, et al. MicroRNAs and complex diseases: from experimental results to computational models. Brief Bioinform 2019; 20:515–39. DOI: https://doi.org/10.1093/bib/bbx130
[17] Chen L, Heikkinen L, Wang C, et al. Trends in the development of miRNA bioinformatics tools. Brief Bioinform 2019;20: 1836-52. DOI: https://doi.org/10.1093/bib/bby054
[18] Chen X, Yan CC, Zhang X, et al. Long non-coding RNAs and complex diseases: from experimental results to computational models. Brief Bioinform 2017; 18:558–76. DOI: https://doi.org/10.1093/bib/bbw060
[19] Signal B, Gloss BS, Dinger ME. Computational approaches for functional prediction and characterisation of long noncoding RNAs. Trends Genet 2016; 32:620-37. DOI: https://doi.org/10.1016/j.tig.2016.08.004
[20] Chen L, Wang C, Sun H, et al. The bioinformatics toolbox for circRNA discovery and analysis. Brief Bioinform 2021; 22: 1706–28. DOI: https://doi.org/10.1093/bib/bbaa001
[21] Chen J, Lin J, Yongfei H, et al. RNA Disease v4.0: an updated resource of RNA-associated diseases, providing RNA-disease analysis, enrichment and prediction. Nucleic Acids Res 2023;51(D1): D1397–404. DOI: https://doi.org/10.1093/nar/gkac814
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
