Generative models for synthetic data generation: application to pharmacokinetic/pharmacodynamic data

Reynolds DA (2009) Gaussian mixture models. Encyclopedia Biometr 741:659–663

Article  Google Scholar 

Elkan C (2010) Expectation maximization algorithm. Encyclopedia Machine Learning. https://cseweb.ucsd.edu/~elkan/250Bfall2007/em.pdf. Accessed 12 December 2023

Lavielle M (2014) Mixed effects models for the population approach: models, tasks, methods and tools. CRC, Boca Raton, Florida

Book  Google Scholar 

Kingma DP, Welling M (2013) Auto-encoding variational bayes. Clin Orthop Relat Res arXiv Preprint arXiv:1312.6114.

Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville AC, Bengio Y (2014) Generative adversarial nets. Adv Neural Inf Process Syst 27

Rezende DJ, Mohamed S (2015) Variational inference with normalizing flows. In: International Conference on Machine Learning, PMLR, pp. 1530–1538

Dhariwal P, Nichol A (2021) Diffusion models beat gans on image synthesis. Adv Neural Inf Process Syst 34:8780–8794

Google Scholar 

Ghosheh GO, Li J, Zhu T (2022) A review of generative adversarial networks for electronic health records: applications, evaluation measures and data sources. arXiv preprint arXiv:2203.07018.

Haarburger C, Horst N, Truhn D, Broeckmann M, Schrading S, Kuhl CK, Merhof D (2019) Multiparametric magnetic resonance image synthesis using generative adversarial networks. In: Eurographics Workshop on Visual Computing for Biomedicine, VCBM, pp 11–15

Delaney AM, Brophy E, Ward TE (2019) Synthesis of realistic ECG using generative adversarial networks. arXiv preprint arXiv:1909.09150.

Motamed S, Rogalla P, Khalvati F (2021) Data augmentation using generative adversarial networks (gans) for Gan-based detection of pneumonia and Covid-19 in chest x-ray images. Inf Med Unlocked 27:100779. https://doi.org/10.1016/j.imu.2021.100779

Abbasi M et al (2022) Designing optimized drug candidates with generative Adversarial Network. J Cheminform 14(1):40

Article  PubMed  PubMed Central  Google Scholar 

Yang H et al (2021) Subtype-GAN: a deep learning approach for integrative cancer subtyping of multi-omics data. Bioinformatics 37(16):2231–2237

Article  PubMed  Google Scholar 

Pauley M et al (2023) T1dCteGui: a user-friendly clinical trial Enrichment Tool to optimize T1D Prevention studies by leveraging AI/ML based Synthetic Patient Population. Clinical Pharmacology & Therapeutics

Google Scholar 

Patki N, Wedge R, Veeramachaneni K (2016) The synthetic data vault. In: IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2016, pp 399–410. https://doi.org/10.1109/DSAA.2016.49.  https://doi.org/10.1214/12-AOS1037 

Zwep LB et al (2022) Virtual patient simulation using copula modeling. Clinical Pharmacology & Therapeutics

Google Scholar 

Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

Article  CAS  PubMed  Google Scholar 

Yoon J, Jarrett D, Van der Schaar M (2019) Time-series generative adversarial networks. Adv Neural Inf Process Syst 32

Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784

Simulx is an easy efficient and flexible application for clinical trial simulations. https://lixoft.com/products/simulx/. Accessed 29 November 2023

de Myttenaere A, Golden B, Le Grand B, Rossi F (2016) Mean absolute percentage error for regression models. Neurocomputing 192:38–48. https://doi.org/10.1016/j.neucom.2015.12.114

Xu B, Wang N, Chen T, Li M (2015) Empirical evaluation of rectified activations in convolutional network. arXiv Preprint arXiv:1505.00853.

Jacobs F, D’Amico S, Benvenuti C, Gaudio M, Saltalamacchia G, Miggiano C, Zambelli A (2023) Opportunities and challenges of synthetic data generation in oncology. JCO Clinical Cancer Informatics. 7:e2300045

Article  PubMed  Google Scholar 

Parikh J, Rumbell T, Butova X, Myachina T, Acero JC, Khamzin S, Gurev V (2022) Generative adversarial networks for construction of virtual populations of mechanistic models: simulations to study Omecamtiv Mecarbil action. J Pharmacokinet Pharmacodyn 49(1):51–64

Article  PubMed  Google Scholar 

D’amico S, Dall’Olio D, Sala C, Dall’Olio L, Sauta E, Zampini M, G. Della Porta M (2023) Synthetic data generation by artificial intelligence to accelerate research and precision medicine in hematology. JCO Clin Cancer Inf 7:e2300021

Shin E, Ramanathan M (2023) Evaluation of prompt engineering strategies for pharmacokinetic data analysis with the ChatGPT large language model. J Pharmacokinet Pharmacodyn, 1–8

Oriol B, Miot A (2021) On some theoretical limitations of Generative Adversarial Networks. arXiv preprint arXiv:2110.10915.

Comments (0)

No login
gif