Login Register Cart 0
Research Article

Predicting LncRNA-Disease Association Based on Generative Adversarial Network

Author(s): Biao Du, Lin Tang, Lin Liu* and Wei Zhou*

Volume 22, Issue 2, 2022

Published on: 15 June, 2021

Page: [144 - 151] Pages: 8

DOI: 10.2174/1566523221666210506131055

Price: $65

Abstract

Background: Increasing research reveals that long non-coding RNAs (lncRNAs) play an important role in various biological processes of human diseases. Nonetheless, only a handful of lncRNA-disease associations have been experimentally verified. The study of lncRNA-disease association prediction based on the computational model has provided a preliminary basis for biological experiments to a great degree so as to cut down the huge cost of wet lab experiments.

Objective: This study aims to learn the real distribution of lncRNA-disease association from a limited number of known lncRNA-disease association data. This paper proposes a new lncRNA-disease association prediction model called LDA-GAN based on a Generative Adversarial Network (GAN).

Methods: Aiming at the problems of slow convergence rate, training instabilities, and unavailability of discrete data in traditional GAN, LDA-GAN utilizes the Gumbel-softmax technology to construct a differentiable process for simulating discrete sampling. Meanwhile, the generator and the discriminator of LDA-GAN are integrated to establish the overall optimization goal based on the pairwise loss function.

Results: Experiments on standard datasets demonstrate that LDA-GAN achieves not only high stability and high efficiency in the process of confrontation learning but also gives full play to the semisupervised learning advantage of generative adversarial learning framework for unlabeled data, which further improves the prediction accuracy of lncRNA-disease association. Besides, case studies show that LDA-GAN can accurately generate potential diseases for several lncRNAs.

Conclusion: We introduce a generative adversarial model to identify lncRNA-disease associations.

Keywords: Generate adversarial network, LncRNA, disease, pairwise loss, generator, discriminator.

« Previous Next »
Graphical Abstract

[1]
Yanofsky C. Establishing the triplet nature of the genetic code. Cell 2007; 128: 815-8.
[http://dx.doi.org/10.1016/j.cell.2007.02.029]
[2]
Merry CR, Niland C, Khalil AM. Diverse functions and mechanisms of mammalian long noncoding RNAs. New York, NY, USA: Springer 2015; pp. 1-14.
[http://dx.doi.org/10.1007/978-1-4939-1369-5_1]
[3]
Zou L, Wang YF. Research progress of long noncoding RNA in autoimmune diseases Basic & Clinical Medicine. 2016; pp. 1441-5. Available at: http://journal11.magtechjournal.com/Jwk_jcyxylc/EN/
[4]
Cheetham S, Gruhl F, Mattick J, Dinger M. Long noncoding RNAs and the genetics of cancer. Br J Cancer 2013; 108: 2419.
[http://dx.doi.org/10.1038/bjc.2013.233]
[5]
Taft RJ, Pang KC, Mercer TR, Dinger M, Mattick JS. Non-coding RNAs: Regulators of disease. J Pathol 2010; 220: 126-39.
[http://dx.doi.org/10.1002/path.2638]
[6]
Huang X, Luo YL, Mao YS, et al. The link between long noncoding RNAs and depression. Prog Neuropsychopharmacol Biol Psychiatry 2017; 73: 73-8.
[http://dx.doi.org/10.1016/j.pnpbp.2016.06.004]
[7]
Yu L, Wu YM, Wu BL, et al. Genetic architecture, epigenetic influence and environment exposure in the pathogenesis of Autism. Sci China Life 2015; 58(10): 958-67.
[http://dx.doi.org/10.1007/s11427-015-4941-1]
[8]
Pasmant E, Sabbagh A, Vidaud M, Bièche I. ANRIL, a long, noncoding RNA, is an unexpected major hotspot in GWAS. FASEB J 2011; 25: 444-8.
[http://dx.doi.org/10.1096/fj.10-172452]
[9]
Zhang Q, Chen CY, Yedavalli VS, Jeang KT. NEAT1 long noncoding RNA and paraspeckle bodies modulate HIV-1 posttranscriptional expression. MBio 2013; 4: e00596-12.
[http://dx.doi.org/10.1128/mBio.00596-12]
[10]
Wapinski O, Chang HY. Long noncoding RNAs and human disease. Trends Cell Biol 2011; 21: 354-61.
[http://dx.doi.org/10.1016/j.tcb.2011.04.001]
[11]
Cui Z, Ren S, Lu J, et al. The prostate cancer-up-regulated long noncoding RNA PlncRNA-1 modulates apoptosis and proliferation through reciprocal regulation of androgen receptor. Urol Oncol Semin Orig Investig 2013; 31: 1117-23.
[http://dx.doi.org/10.1016/j.urolonc.2011.11.030]
[12]
Ma Z, Xue S, Zeng B, Qiu D. lncRNA SNHG5 is associated with poor prognosis of bladder cancer and promotes bladder cancer https://doi.org/10.3892/ol.2017.7527
[13]
Chen X, Yan GY. Novel human lncRNA-disease association inference based on lncRNA expression profiles. Bioinformatics 2013; 29: 2617-24.
[http://dx.doi.org/10.1093/bioinformatics/btt426]
[14]
Lan W, Li M, Zhao K, et al. LDAP: A web server for lncRNA-disease association prediction. Bioinformatics 2016; 33: 458-60.
[http://dx.doi.org/10.1093/bioinformatics/btw639]
[15]
Zhou M, Wang X, Li J, et al. Prioritizing candidate disease related long non-coding RNAs by walking on the heterogeneous lncRNA and disease network. Mol Biosyst 2015; 11(3): 760-9.
[http://dx.doi.org/10.1039/C4MB00511B]
[16]
Ding L, Wang M, Sun D, Li A. TPGLDA: Novel prediction of associations between lncRNAs and diseases via lncRNA-disease-gene tripartite graph. Sci Rep 2018; 8: 1065.
[http://dx.doi.org/10.1038/s41598-018-19357-3]
[17]
Sun J, Shi HB, Wang ZZ, et al. Inferring novel lncRNA-disease associations based on a random walk model of a lncRNA functional similarity network. Mol Biosyst 2014; 10(8): 2074-81.
[http://dx.doi.org/10.1039/C3MB70608G]
[18]
Arjovsky M, Bottou L. Towards principled methods for training generative adversarial networks. Proceedings of the 5th International Conference on Learning Representations 2017; 1-17. Available at: https://openreview.net/pdf?id=Hk4_qw5xe
[19]
Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. Proceedings of the Conference on Advances in Neural Information Processing Systems 2672-80 Available at: https://arxiv.org/pdf/1406.2661.pdf
[20]
Sun Z, Wu B, Wu Y, et al. APL: Adversarial Pairwise Learning for Recommender Systems. [J] Expert Syst Appl 2019; 118(MAR): 573-84.
[21]
Jang E, Gu S, Poole B. Categorical reparameterization with global-softmax. Proceeding of the 5th International Conference on Learning Repersentations. Available at: https://openreview.net/pdf?id=rkE3y85ee
[22]
Rendle S, Freudenthaler C, Gantner Z, et al. BPR: bayesian personalized ranking from implicit feedback. Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. 452-61. Available at: https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf
[23]
Rendle S, Freudenthaler C. Improving pairwise learning for item recommendation from implicit feedback. Proceedings of the 7th ACM International Conference on Web Search and Data Mining. 273-82.
[http://dx.doi.org/10.1145/2556195.2556248]
[24]
Arjovsky M, Chintala S, Bottou L. Wasserstein generative adversarial networks. Proceedings of the 34th International Conference on Machine Learning 2017; 70: 214-3. Available at: http://proceedings.mlr.press/v70/arjovsky17a.html
[25]
Fu G, Wang J, Domeniconi C, Yu G. Matrix factorization-based data fusion for the prediction of lncRNA-disease associations. Bioinformatics 2017; 34: 1529-37.
[http://dx.doi.org/10.1093/bioinformatics/btx794]
[26]
Chen G, Wang ZY, Wang DQ, et al. LncRNADisease: a database for long-non-coding RNA-associated diseases. Nucleic Acids Res 2013; 41(D1): D983-6.
[http://dx.doi.org/10.1093/nar/gks1099]
[27]
Chen X. KATZLDA: KATZ measure for the lncRNA-disease association prediction. Sci Rep 2015.
[http://dx.doi.org/10.1038/srep16840]
[28]
Chen X, You ZH, Yan GY, et al. IRWRLDA: improved random walk with restart for lncRNA-disease association prediction. Oncotarget 2016; 7(36): 57919-31.
[http://dx.doi.org/10.18632/oncotarget.11141]
[29]
Huang YA, Chen X. ILNCSIM: improved lncRNA functional similarity calculation model. Oncotarget 2015; 7: 25902-14.
[http://dx.doi.org/10.18632/oncotarget.8296]
[30]
Wang H, Huang H, Ding C, et al. Predicting protein-protein interactions from multimodal biological data sources via nonnegative matrix tri-factorization. J Comput Biol 20: 344-58.
[http://dx.doi.org/10.1089/cmb.2012.0273]
[31]
Zhao YL, Ai YQ. Corrigendum to “Knockdown of lncRNA MALAT1 promotes bupivacaine-induced neurotoxicity via the miR101-3p/PDCD4 axis” life science 2020; 253: 117769. http://dx.doi.org/10.1016/j.lfs.2020.117769
[32]
Michael A, Gordon Beatrice B, Dawn R, et al. Cochrane The long non‐coding RNA MALAT1 promotes ovarian cancer progression by regulating RBFOX2‐mediated alternative splicing. Mol Carcinog 2019; 58(2): 196-205.
[http://dx.doi.org/10.1002/mc.22919.]
[33]
Lian TT, Mi CY, Xie JY, et al. Function and mechanism of lncRNA in malignant tumors of female reproductive system. Huanjing Yu Zhiye Yixue 2019; 36(3): 232-41.
[http://dx.doi.org/10.3389/fphys.2018.00321]
[34]
Sheng CX, Li HH, Ma L. Research progress on the role and mechanism of lncRNA BCYRN1 in diseases. Journal of Nanchang University (Medical Edition) 2019; 59(02): 97-99 + 103. http://dx.doi.org/10.1016/j.molimm.2018.05.030.
[35]
Anirban R, Sudip S, Pijush D, et al. Deregulation of H19 is associated with cervical carcinoma. Genomics 2020; 112(1): 9641-970.
[http://dx.doi.org/10.1016/j.ygeno.2019.06.012]
[36]
Bruno C, Blagoskonov O, Barberet J, et al. Sperm imprinting integrity in seminoma patients? Clin Epigenetics 2018; 10(1)
[http://dx.doi.org/10.1186/s13148-018-0559-z]

Rights & Permissions Print Cite
Article Metrics
25
1
© 2024 Bentham Science Publishers | Privacy Policy