Artificial intelligence‐driven consensus gene signatures for improving bladder cancer clinical outcomes identified by multi‐center integration analysis

To accurately predict the prognosis and further improve the clinical outcomes of bladder cancer (BLCA), we leveraged large‐scale data to develop and validate a robust signature consisting of small gene sets. Ten machine‐learning algorithms were enrolled and subsequently transformed into 76 combinati...

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Detalles Bibliográficos
Autores principales: Xu, Hui, Liu, Zaoqu, Weng, Siyuan, Dang, Qin, Ge, Xiaoyong, Zhang, Yuyuan, Ren, Yuqing, Xing, Zhe, Chen, Shuang, Zhou, Yifang, Ren, Jianzhuang, Han, Xinwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Research Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9718116/
https://www.ncbi.nlm.nih.gov/pubmed/36083778
http://dx.doi.org/10.1002/1878-0261.13313

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9718116/
https://www.ncbi.nlm.nih.gov/pubmed/36083778
http://dx.doi.org/10.1002/1878-0261.13313

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