SAGL: A self-attention-based graph learning framework for predicting survival of colorectal cancer patients.

Journal: Computer methods and programs in biomedicine
PMID: 38583291

Abstract

BACKGROUND AND OBJECTIVE: Colorectal cancer (CRC) is one of the most commonly diagnosed cancers worldwide. The accurate survival prediction for CRC patients plays a significant role in the formulation of treatment strategies. Recently, machine learning and deep learning approaches have been increasingly applied in cancer survival prediction. However, most existing methods inadequately represent and leverage the dependencies among features and fail to sufficiently mine and utilize the comorbidity patterns of CRC. To address these issues, we propose a self-attention-based graph learning (SAGL) framework to improve the postoperative cancer-specific survival prediction for CRC patients.

Authors

  • Ping Yang
    Key Laboratory of Grain and Oil Processing and Food Safety of Sichuan Province, College of Food and Bioengineering, Xihua University Chengdu 610039 China xingyage1@163.com.
  • Hang Qiu
    School of Computer Science and Engineering, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731, Chengdu, Sichuan, P.R. China. qiuhang@uestc.edu.cn.
  • Xulin Yang
    School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
  • Liya Wang
    Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China.
  • Xiaodong Wang
    Cardiovascular Department, TEDA International Cardiovascular Hospital, Tianjin, China.

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