Transcriptomic Deconvolution of Neuroendocrine Neoplasms Predicts Clinically Relevant Characteristics

SIMPLE SUMMARY: Rapidly growing neuroendocrine neoplasms (NEN) often defy easy classification by the pathologist. Machine learning approaches can improve the classification’s accuracy, but these generally require large amounts of training data. As tumor-based training data will remain sparse for ver...

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Detalles Bibliográficos
Autores principales: Otto, Raik, Detjen, Katharina M., Riemer, Pamela, Fattohi, Melanie, Grötzinger, Carsten, Rindi, Guido, Wiedenmann, Bertram, Sers, Christine, Leser, Ulf
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9913692/
https://www.ncbi.nlm.nih.gov/pubmed/36765893
http://dx.doi.org/10.3390/cancers15030936

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9913692/
https://www.ncbi.nlm.nih.gov/pubmed/36765893
http://dx.doi.org/10.3390/cancers15030936

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