Prognostic risk modeling of endometrial cancer using programmed cell death-related genes: a comprehensive machine learning approach

Abstract Background Endometrial cancer represents a significant health challenge, with rising incidence and complex prognostic challenges. This study aimed to develop a robust predictive model integrating programmed cell death-related genes and advanced machine learning techniques. Methods Utilizing...

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Detalhes bibliográficos

Principais autores:	Tianshu Chen, Yuhan Yang, Zhizhong Huang, Feng Pan, Zhendi Xiao, Kunxue Gong, Wenguang Huang, Liu Xu, Xueqin Liu, Caiyun Fang
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Springer 2025-03-01
coleção:	Discover Oncology
Assuntos:	Endometrial cancer Prognostic modeling Programmed cell death Machine learning Precision oncology
Acesso em linha:	https://doi.org/10.1007/s12672-025-02039-8
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