Optimized cell type signatures revealed from single-cell data by combining principal feature analysis, mutual information, and machine learning

Machine learning techniques are excellent to analyze expression data from single cells. These techniques impact all fields ranging from cell annotation and clustering to signature identification. The presented framework evaluates gene selection sets how far they optimally separate defined phenotypes...

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Detalles Bibliográficos
Autores principales: Caliskan, Aylin, Caliskan, Deniz, Rasbach, Lauritz, Yu, Weimeng, Dandekar, Thomas, Breitenbach, Tim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2023
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10276237/
https://www.ncbi.nlm.nih.gov/pubmed/37333862
http://dx.doi.org/10.1016/j.csbj.2023.06.002

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10276237/
https://www.ncbi.nlm.nih.gov/pubmed/37333862
http://dx.doi.org/10.1016/j.csbj.2023.06.002

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