Synthetic data: how could it be used in infectious disease research?

doi:10.1080/17460913.2024.2400853

. 2024;19(17):1439-1444.

doi: 10.1080/17460913.2024.2400853. Epub 2024 Sep 30.

Synthetic data: how could it be used in infectious disease research?

Styliani-Christina Fragkouli^{1

2}, Dhwani Solanki³, Leyla J Castro³, Fotis E Psomopoulos², Núria Queralt-Rosinach⁴, Davide Cirillo⁵, Lisa C Crossman^{6

7}

Affiliations

¹ Department of Biology, National & Kapodistrian University of Athens, Athens, 15772, Greece.
² Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thessaloniki, 57001, Greece.
³ ZB MED Information Centre for Life Sciences, Cologne, 50931, Germany.
⁴ Department of Human Genetics, Leiden University Medical Center, Leiden, 2333, The Netherlands.
⁵ Barcelona Supercomputing Center (BSC), Barcelona, E-08034, Spain.
⁶ SequenceAnalysis.co.uk, Norwich Research Park, Norwich, NR4 7UG, UK.
⁷ School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK.

PMID: 39345126
PMCID: PMC11492709 (available on 2025-09-30)
DOI: 10.1080/17460913.2024.2400853

Synthetic data: how could it be used in infectious disease research?

Styliani-Christina Fragkouli et al. Future Microbiol. 2024.

. 2024;19(17):1439-1444.

doi: 10.1080/17460913.2024.2400853. Epub 2024 Sep 30.

Authors

Styliani-Christina Fragkouli^{1

2}, Dhwani Solanki³, Leyla J Castro³, Fotis E Psomopoulos², Núria Queralt-Rosinach⁴, Davide Cirillo⁵, Lisa C Crossman^{6

7}

Affiliations

¹ Department of Biology, National & Kapodistrian University of Athens, Athens, 15772, Greece.
² Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thessaloniki, 57001, Greece.
³ ZB MED Information Centre for Life Sciences, Cologne, 50931, Germany.
⁴ Department of Human Genetics, Leiden University Medical Center, Leiden, 2333, The Netherlands.
⁵ Barcelona Supercomputing Center (BSC), Barcelona, E-08034, Spain.
⁶ SequenceAnalysis.co.uk, Norwich Research Park, Norwich, NR4 7UG, UK.
⁷ School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK.

PMID: 39345126
PMCID: PMC11492709 (available on 2025-09-30)
DOI: 10.1080/17460913.2024.2400853

No abstract available

Keywords: bacteria; deep learning; healthcare; infection; infectious disease; machine learning; microbiology; synthetic data.

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Conflict of interest statement

The authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

References

1. Vaswani A, Shazeer N, Parmar N, et al. . Attention is all you need. Adv Neural Inf Process Syst. 2017. NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems.. 6000–6010.
1. Chen RJ, Lu MY, Chen TY, et al. . Synthetic data in machine learning for medicine and healthcare. Nat Biomed Eng. 2021;5:493–497. doi:10.1038/s41551-021-00751-8 - DOI - PMC - PubMed
2. •• Highlights the current trends, challenges and vulnerabilities of the implementation of synthetic data in medicine and healthcare.
1. D'Amico S, Dall'Olio D, Sala C, et al. . Synthetic Data Generation by Artificial Intelligence to Accelerate Research and Precision Medicine in Hematology. JCO Clin Cancer Inform. 2023;7:e2300021. doi:10.1200/CCI.23.00021 - DOI - PMC - PubMed
2. • The main takeaway from this paper is the novel AI-driven method for generating synthetic patient data that closely replicates actual clinical-genomic characteristics and outcomes while maintaining privacy. Additionally, that this technology has the potential to expedite both translational research and clinical trials in the field of hematology.
1. Giuffrè M, Shung DL. Harnessing the power of synthetic data in healthcare: innovation, application, and privacy. NPJ Digit Med. 2023;6(186). doi:10.1038/s41746-023-00927-3 - DOI - PMC - PubMed
1. Akkem Y, Biswas SK, Varanasi A. A comprehensive review of synthetic data generation in smart farming by using variational autoencoder and generative adversarial network. Eng Appl Artif Intell. 2024;131:107881. doi:10.1016/j.engappai.2024.107881 - DOI

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[1] Vaswani A, Shazeer N, Parmar N, et al. . Attention is all you need. Adv Neural Inf Process Syst. 2017. NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems.. 6000–6010.

[2] Vaswani A, Shazeer N, Parmar N, et al. . Attention is all you need. Adv Neural Inf Process Syst. 2017. NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems.. 6000–6010.

[3] Chen RJ, Lu MY, Chen TY, et al. . Synthetic data in machine learning for medicine and healthcare. Nat Biomed Eng. 2021;5:493–497. doi:10.1038/s41551-021-00751-8 - DOI - PMC - PubMed

•• Highlights the current trends, challenges and vulnerabilities of the implementation of synthetic data in medicine and healthcare.

[4] Chen RJ, Lu MY, Chen TY, et al. . Synthetic data in machine learning for medicine and healthcare. Nat Biomed Eng. 2021;5:493–497. doi:10.1038/s41551-021-00751-8 - DOI - PMC - PubMed

[5] •• Highlights the current trends, challenges and vulnerabilities of the implementation of synthetic data in medicine and healthcare.

[6] D'Amico S, Dall'Olio D, Sala C, et al. . Synthetic Data Generation by Artificial Intelligence to Accelerate Research and Precision Medicine in Hematology. JCO Clin Cancer Inform. 2023;7:e2300021. doi:10.1200/CCI.23.00021 - DOI - PMC - PubMed

• The main takeaway from this paper is the novel AI-driven method for generating synthetic patient data that closely replicates actual clinical-genomic characteristics and outcomes while maintaining privacy. Additionally, that this technology has the potential to expedite both translational research and clinical trials in the field of hematology.

[7] D'Amico S, Dall'Olio D, Sala C, et al. . Synthetic Data Generation by Artificial Intelligence to Accelerate Research and Precision Medicine in Hematology. JCO Clin Cancer Inform. 2023;7:e2300021. doi:10.1200/CCI.23.00021 - DOI - PMC - PubMed

[8] • The main takeaway from this paper is the novel AI-driven method for generating synthetic patient data that closely replicates actual clinical-genomic characteristics and outcomes while maintaining privacy. Additionally, that this technology has the potential to expedite both translational research and clinical trials in the field of hematology.

[9] Giuffrè M, Shung DL. Harnessing the power of synthetic data in healthcare: innovation, application, and privacy. NPJ Digit Med. 2023;6(186). doi:10.1038/s41746-023-00927-3 - DOI - PMC - PubMed

[10] Giuffrè M, Shung DL. Harnessing the power of synthetic data in healthcare: innovation, application, and privacy. NPJ Digit Med. 2023;6(186). doi:10.1038/s41746-023-00927-3 - DOI - PMC - PubMed

[11] Akkem Y, Biswas SK, Varanasi A. A comprehensive review of synthetic data generation in smart farming by using variational autoencoder and generative adversarial network. Eng Appl Artif Intell. 2024;131:107881. doi:10.1016/j.engappai.2024.107881 - DOI

[12] Akkem Y, Biswas SK, Varanasi A. A comprehensive review of synthetic data generation in smart farming by using variational autoencoder and generative adversarial network. Eng Appl Artif Intell. 2024;131:107881. doi:10.1016/j.engappai.2024.107881 - DOI

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Synthetic data: how could it be used in infectious disease research?

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Synthetic data: how could it be used in infectious disease research?

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