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一个适应多任务领域的模型,用于预测化学疗法的反应,从反复改变的癌症基因中的突变中预测
Aishwarya Jayagopal1, Robert J Walsh2, Krishna Kumar Hariprasannan1
1Department of Information Systems and Analytics, School of Computing, National University of Singapore, Singapore 117417, Singapore.
iScience
|March 31, 2025
概括
一个新的药物反应预测 (DRP) 模型,DruID,有效地预测化疗反应使用临床级下一代测序 (cNGS) 的有限基因组数据. 这为缺乏向治疗选择的患者提供了个性化的瘤学.
科学领域:
- 在瘤学瘤学.
- 基因组学就是基因组学.
- 计算生物学 计算生物学
背景情况:
- 下一代测序 (NGS) 在瘤学中至关重要,但大多数患者不会从向治疗中受益.
- 现有的药物反应预测 (DRP) 模型通常依赖于全面的基因组数据,这并不总是临床上可用的.
- 需要使用临床可行数据的DRP模型,例如临床级NGS (cNGS) 面板的受限基因组,用于化疗重定位.
研究的目的:
- 开发一种能够使用cNGS面板数据预测药物反应的DRP模型.
- 为了应对数据稀缺和有限的患者药物反应信息在DRP模型开发中的挑战.
- 为了评估DRP模型的性能,使用整个exome与cNGS数据.
主要方法:
- 证明现有的DRP模型与整个exome和cNGS数据的性能可比.
- 引入了药物识别器 (DruID),这是一个新的DRP模型,专为受限制的基因组设计.
- 在DruID中,雇员转移学习,变量注释,域不变表示学习和多任务学习.
主要成果:
- 现有的DRP模型使用全外因子测序和cNGS数据显示了类似的性能.
- 在胰腺癌数据集上,DruID显著超过了最先进的DRP方法.
- DruID在两个独立的现实世界临床数据集上展示了强大的药物反应分类.
结论:
- 通过使用cNGS面板的有限基因组,可以有效地开发DRP模型.
- DruID代表了临床上适用的DRP工具用于化疗重定位的重大进展.
- 这种方法增强了针对更广泛的癌症患者群体个性化治疗策略的潜力.
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