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

BACKGROUND: It remains an important challenge to predict the functional consequences or clinical impacts of genetic variants in human diseases, such as cancer. An increasing number of genetic variants in cancer have been discovered and documented in public databases such as COSMIC, but the vast majo...

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Detalles Bibliográficos
Autores principales: Ren, Zilin, Li, Quan, Cao, Kajia, Li, Marilyn M., Zhou, Yunyun, Wang, Kai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9909865/
https://www.ncbi.nlm.nih.gov/pubmed/36759776
http://dx.doi.org/10.1186/s12859-023-05141-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9909865/
https://www.ncbi.nlm.nih.gov/pubmed/36759776
http://dx.doi.org/10.1186/s12859-023-05141-2

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