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Machine learning developed an intratumor heterogeneity signature for predicting clinical outcome and immunotherapy benefit in bladder cancer.

Cheng ChenJun ZhangXiaoshuang LiuQianfeng ZhuangHao LuJianquan Hou
Published in: Translational andrology and urology (2024)
The current study developed an optimal IRS for bladder cancer patients, which acted as an indicator for predicting prognosis, stratifying risk and guiding treatment for bladder cancer patients. Further analysis should be focused on the exploration the differentially expressed genes (DEGs) and related underlying mechanism mediating the development of bladder cancer in different IRS score group.
Keyphrases
  • machine learning
  • spinal cord injury
  • single cell
  • genome wide
  • artificial intelligence
  • combination therapy
  • gene expression
  • deep learning
  • dna methylation
  • drug induced
  • replacement therapy