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Correction: Model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data
| Autores principales: | , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10230664/ https://www.ncbi.nlm.nih.gov/pubmed/37259034 http://dx.doi.org/10.1186/s12859-023-05357-2 |
| _version_ | 1785051582653530112 |
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| author | Ren, Zilin Li, Quan Cao, Kajia Li, Marilyn M. Zhou, Yunyun Wang, Kai |
| author_facet | Ren, Zilin Li, Quan Cao, Kajia Li, Marilyn M. Zhou, Yunyun Wang, Kai |
| author_sort | Ren, Zilin |
| collection | PubMed |
| description | |
| format | Online Article Text |
| id | pubmed-10230664 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2023 |
| publisher | BioMed Central |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-102306642023-06-01 Correction: Model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data Ren, Zilin Li, Quan Cao, Kajia Li, Marilyn M. Zhou, Yunyun Wang, Kai BMC Bioinformatics Correction BioMed Central 2023-05-31 /pmc/articles/PMC10230664/ /pubmed/37259034 http://dx.doi.org/10.1186/s12859-023-05357-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
| spellingShingle | Correction Ren, Zilin Li, Quan Cao, Kajia Li, Marilyn M. Zhou, Yunyun Wang, Kai Correction: Model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data |
| title | Correction: Model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data |
| title_full | Correction: Model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data |
| title_fullStr | Correction: Model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data |
| title_full_unstemmed | Correction: Model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data |
| title_short | Correction: Model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data |
| title_sort | correction: model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data |
| topic | Correction |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10230664/ https://www.ncbi.nlm.nih.gov/pubmed/37259034 http://dx.doi.org/10.1186/s12859-023-05357-2 |
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