A multi-task deep learning model based on comprehensive feature integration and self-attention mechanism for predicting response to anti-PD1/PD-L1.

Journal: International immunopharmacology
PMID: 39265355

Abstract

BACKGROUND: Immune checkpoint inhibitor (ICI) has been widely used in the treatment of advanced cancers, but predicting their efficacy remains challenging. Traditional biomarkers are numerous but exhibit heterogeneity within populations. For comprehensively utilizing the ICI-related biomarkers, we aim to conduct multidimensional feature selection and deep learning model construction.

Authors

  • Ren Wang
    School of Medicine, Jiangsu University, Zhenjiang 212013, China.
  • Qiumei Liu
    The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Department of Immunology, School of Basic Medical Sciences, Nanjing Medical University, Nanjing, China; The Affiliated Huai'an No. 1 People's Hospital, Nanjing Medical University, Huai'an, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.
  • Wenhua You
    The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Department of Immunology, School of Basic Medical Sciences, Nanjing Medical University, Nanjing, China; The Affiliated Huai'an No. 1 People's Hospital, Nanjing Medical University, Huai'an, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.
  • Yun Chen

Keywords

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