通过样本合成来改善患者药物反应预测的解生成模型
Kunshi Li1, Bihan Shen1, Fangyoumin Feng1
1CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
Journal of pharmaceutical analysis
|July 18, 2025
概括
一个新的网络,DiSyn,通过将临床前模型的知识转移到患者身上,改善了个性化癌症药物反应预测. 这种方法提高了准确性,并显示了生物标志物发现的潜力.
科学领域:
- 计算生物学是一种计算生物学.
- 精准医学是一门精准的医学.
- 癌症研究 癌症研究
背景情况:
- 基于分子数据的个性化药物反应预测对于精确的癌症医学至关重要.
- 当前的计算方法在临床应用中面临挑战,原因是临床前模型和患者之间的差异.
研究的目的:
- 开发一种新的脱合成传输网络 (DiSyn),用于准确预测药物反应.
- 能够有效地将从临床前模型的学习转移到癌症治疗中的临床患者.
主要方法:
- DiSyn使用域分离网络 (DSN) 来分离药物反应特征.
- 采用数据合成来增加样本大小和代训练来改善特征解.
- 在大型未标记癌症样本上进行预训练,并通过TCGA,I-SPY2和NIBR PDXE数据集进行验证.
主要成果:
- 与最先进的方法相比,DiSyn在预测癌症患者和小鼠的药物反应方面取得了竞争性表现.
- 证明了成千上万乳腺癌患者药物反应的异质性.
- 突出了生物标志物发现和药物组合预测的潜在应用.
结论:
- 在癌症药物反应预测方面,DiSyn为弥合临床前模型和临床患者之间的差距提供了一个有希望的方法.
- 该方法具有显著的潜力,可以通过改进的个性化治疗策略来推进精准医学.
- 进一步应用DiSyn可以加速生物标志物的发现,并优化癌症的药物组合疗法.
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