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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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-03-01
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| Series: | Discover Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s12672-025-02039-8 |
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