Learning torus PCA based classification for multiscale RNA correction with application to SARS-CoV-2

Wiechers, H, Eltzner, B, Mardia, KV orcid.org/0000-0003-0090-6235 et al. (1 more author) (2023) Learning torus PCA based classification for multiscale RNA correction with application to SARS-CoV-2. Journal of the Royal Statistical Society: Series C, 72 (2). pp. 271-293. ISSN 0035-9254

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Copyright, Publisher and Additional Information: © (RSS) Royal Statistical Society 2023. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https:// creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: angular shape analysis, clash correction, frameshift stimulation element, Fréchet and Procrustes means, mesoscopic shape and microscopic shape, size-and-shape space
Dates:
  • Accepted: 20 December 2022
  • Published (online): 24 March 2023
  • Published: May 2023
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 06 Jan 2023 11:45
Last Modified: 05 Oct 2023 12:49
Status: Published
Publisher: Oxford University Press
Identification Number: https://doi.org/10.1093/jrsssc/qlad004

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