<p>Enhancing clinical outcome predictions through effective sample size evaluation in graph-based digital twin modeling</p>

Xi Li; Jui-Hsuan Chang; Mythreye Venkatesan; Zhiping Paul  Wang; Jason H. Moore

doi:10.7490/f1000research.1119989.1

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Li X, Chang JH, Venkatesan M et al. Enhancing clinical outcome predictions through effective sample size evaluation in graph-based digital twin modeling [version 1; not peer reviewed]. F1000Research 2024, 13:1338 (poster) (https://doi.org/10.7490/f1000research.1119989.1)

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Enhancing clinical outcome predictions through effective sample size evaluation in graph-based digital twin modeling

Xi Li, Jui-Hsuan Chang, Mythreye Venkatesan, Zhiping Paul Wang, Jason H. Moore¹

Published 07 Nov 2024 (https://doi.org/10.7490/f1000research.1119989.1)

Author Affiliations

¹ Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California, USA

Copyright © 2024 Li X et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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CITE

Published 07 Nov 2024

Enhancing clinical outcome predictions through effective sample size evaluation in graph-based digital twin modeling
[version 1; not peer reviewed]

Xi Li, Jui-Hsuan Chang, Mythreye Venkatesan, Zhiping Paul Wang, Jason H. Moore¹

Author Affiliations

¹ Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California, USA

Presented at

Pacific Symposium on Biocomputing (PSB) 2025

Competing Interests

No competing interests were disclosed

Keywords

Digital twins, Synthetic data, Effective sample size, Reproducibility, Precision medicine

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