在个性化瘤领域进行导航:通过计算建模克服挑战并拓展视野
Melike Sirlanci1,2, David Albers1,3,4,5, Jennifer Kwak6
1Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States.
Journal of the American Medical Informatics Association : JAMIA
|September 4, 2025
概括
混合建模 (HM) 通过结合机器学习 (ML) 和机械建模 (MM) 来改善罕见疾病的个性化治疗预测. 这种方法解决了临床瘤学新疗法的数据稀缺性和稀缺性问题.
科学领域:
- 计算生物学
- 临床瘤学
- 转化医学
背景情况:
- 对于具有新疗法的罕见疾病来说,个性化预测治疗反应是具有挑战性的.
- 数据稀缺,稀缺性和跨学科合作是关键障碍.
- 机器学习 (ML),机械模型 (MM) 和混合模型 (HM) 提供了潜在的解决方案.
研究的目的:
- 讨论罕见疾病个性化预测的计算建模方面的挑战.
- 提出一个混合建模 (HM) 方法,结合ML和MM.
- 评估HM方法用于预测激进性淋巴瘤的仿真抗原受体T细胞治疗.
主要方法:
- 开发了一种混合建模 (HM) 方法,整合了ML和MM技术.
- 应用HM方法对接受CAR T细胞治疗的13名患者进行研究.
- 将HM的性能与单独的机械模型 (MM) 进行比较.
主要成果:
- 与单独使用MM相比,HM方法显著提高了预测准确性.
- 使用HM,根平均平方误差减少了61.27±23.21%.
- 在细胞计数方面量化性能改善 (MM: 2.36*10^5±1.68*10^5与HM: 9.57*10^4±8.37*10^4细胞).
结论:
- 混合建模 (HM) 有效地解决了罕见疾病个性化预测中的数据稀缺性和稀缺性.
- 它利用了ML和MM的互补优势.
- 开发先进的HM技术需要专门的跨学科团队进行广泛的临床应用.
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