通过使用域调整和原型学习来预测患者临床抗癌药物反应.
IEEE journal of biomedical and health informatics
|September 18, 2024
概括
预测患者的抗癌药物反应对于个性化医学至关重要. 我们的DAPL模型有效地将细胞系数据的知识转移到患者数据,提高预测准确度,以获得更好的癌症治疗策略.
科学领域:
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
- 药物基因组学 药物基因组学
背景情况:
- 准确的抗癌药物反应预测对于个性化癌症治疗至关重要.
- 由于患者数据稀缺,细胞系和患者数据集之间的分布不同,现有的模型扎.
- 目前的转移学习方法受到细胞系数据异常的限制,无法利用未标记的患者数据.
研究的目的:
- 开发一个强大的模型,DAPL,用于预测患者抗癌药物反应.
- 为了应对数据稀缺和领域转移在抗癌药物反应预测方面的挑战.
主要方法:
- DAPL使用多个变异自编码器 (VAE) 从细胞系 (CCLE,GDSC) 和患者 (TCGA,PDTC) 数据中提取域不变特征.
- 使用图形神经网络 (GNN) 提取药物特征.
- 原型学习将这些特征结合起来,训练一个分类器,以改进患者反应预测.
主要成果:
- DAPL模型在预测患者抗癌药物反应方面表现卓越.
- 在跨领域的预测任务中,DAPL的性能优于现有的最先进的方法.
- 该模型有效地处理域位移和数据异常.
结论:
- DAPL为准确预测患者抗癌药物反应提供了一种有希望的方法.
- 该方法通过允许更有效的治疗选择来增强个性化医疗.
- 利用域不变特征和原型学习可以提高模型的稳定性和准确性.
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