Early stage NSCLS patients’ prognostic prediction with multi-information using transformer and graph neural network model

BACKGROUND: We proposed a population graph with Transformer-generated and clinical features for the purpose of predicting overall survival (OS) and recurrence-free survival (RFS) for patients with early stage non-small cell lung carcinomas and to compare this model with traditional models. METHODS:...

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Detalles Bibliográficos
Autores principales: Lian, Jie, Deng, Jiajun, Hui, Edward S, Koohi-Moghadam, Mohamad, She, Yunlang, Chen, Chang, Vardhanabhuti, Varut
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2022
Materias:
Computational and Systems Biology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9531948/
https://www.ncbi.nlm.nih.gov/pubmed/36194194
http://dx.doi.org/10.7554/eLife.80547

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9531948/
https://www.ncbi.nlm.nih.gov/pubmed/36194194
http://dx.doi.org/10.7554/eLife.80547

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