Login Register Cart 0
Research Article

基于生成对抗网络预测 LncRNA-疾病关联

卷 22, 期 2, 2022

发表于: 15 June, 2021

页: [144 - 151] 页: 8

弟呕挨: 10.2174/1566523221666210506131055

价格: $65

摘要

背景：越来越多的研究表明，长链非编码 RNA (lncRNA) 在人类疾病的各种生物学过程中发挥着重要作用。尽管如此，只有少数 lncRNA 与疾病的关联得到了实验验证。基于计算模型的lncRNA-疾病关联预测研究在很大程度上为生物学实验提供了初步依据，从而降低了湿实验室实验的巨大成本。 目的：本研究旨在从有限数量的已知 lncRNA 疾病关联数据中了解 lncRNA 疾病关联的真实分布。本文提出了一种基于生成对抗网络 (GAN) 的新 lncRNA 疾病关联预测模型，称为 LDA-GAN。 方法：针对传统GAN存在收敛速度慢、训练不稳定性、离散数据不可用等问题，LDA-GAN利用Gumbel-softmax技术构建可微分过程来模拟离散采样。同时，将 LDA-GAN 的生成器和判别器集成在一起，建立基于成对损失函数的整体优化目标。 结果：在标准数据集上的实验表明，LDA-GAN不仅在对抗学习过程中实现了高稳定性和高效率，而且充分发挥了生成对抗学习框架对未标记数据的半监督学习优势，进一步提高了预测精度lncRNA-疾病关联。此外，案例研究表明，LDA-GAN 可以准确地为几种 lncRNA 生成潜在疾病。 结论：我们引入了一种生成对抗模型来识别 lncRNA 与疾病的关联。

关键词: 生成对抗网络、LncRNA、疾病、成对损失、生成器、鉴别器

« Previous Next »
图形摘要

[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