A machine learning one-class logistic regression model to predict stemness for single cell transcriptomics and spatial omics

Cell annotation is a crucial methodological component to interpreting single cell and spatial omics data. These approaches were developed for single cell analysis but are often biased, manually curated and yet unproven in spatial omics. Here we apply a stemness model for assessing oncogenic states t...

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Detalles Bibliográficos
Autores principales: Dezem, Felipe Segato, Marção, Maycon, Ben-Cheikh, Bassem, Nikulina, Nadya, Omotoso, Ayodele, Burnett, Destiny, Coelho, Priscila, Hurley, Judith, Gomez, Carmen, Phan-Everson, Tien, Ong, Giang, Martelotto, Luciano, Lewis, Zachary R., George, Sophia, Braubach, Oliver, Malta, Tathiane M., Plummer, Jasmine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10683105/
https://www.ncbi.nlm.nih.gov/pubmed/38017371
http://dx.doi.org/10.1186/s12864-023-09722-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10683105/
https://www.ncbi.nlm.nih.gov/pubmed/38017371
http://dx.doi.org/10.1186/s12864-023-09722-6

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