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. 2023 May 31;23(Suppl 3):572. doi: 10.1186/s12859-023-05357-2

Correction: Model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data

Zilin Ren 1, Quan Li 1,2, Kajia Cao 3, Marilyn M Li 3,4, Yunyun Zhou 1,, Kai Wang 1,4,
PMCID: PMC10230664  PMID: 37259034

Correction : BMC Bioinformatics (2023) 24:43 https://doi.org/10.1186/s12859-023-05141-2

Following publication of the original article [1], it was reported that the article entitled “Model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data” was published in the regular issue of this journal instead of in the supplement issue.

The details of the supplement in which this article ought to have been published are given below:

About this supplement

This article has been published as part of BMC Bioinformatics Volume 23 Supplement 3, 2022: Selected articles from the International Conference on Intelligent Biology and Medicine (ICIBM 2021): bioinformatics. The full contents of the supplement are available online at https://bmcbioinformatics.biomedcentral.com/articles/supplements/volume-23-supplement-3.

The publisher apologizes for any inconvenience caused.

Footnotes

Publisher's Note

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Contributor Information

Yunyun Zhou, Email: zhouy6@chop.edu.

Kai Wang, Email: wangk@chop.edu.

Reference

  • 1.Ren Z, et al. Model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data. BMC Bioinform. 2023;24:43. doi: 10.1186/s12859-023-05141-2. [DOI] [PMC free article] [PubMed] [Google Scholar]

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