可解释的多任务学习通过共享遗传基础改善了对许多疾病的多基因风险评分的并行估计
Adrien Badré1, Chongle Pan1,2
1School of Computer Science, University of Oklahoma, Norman, Oklahoma, United States of America.
PLoS computational biology
|July 7, 2023
概括
这项研究表明,预测多种疾病的疾病风险同时提高了准确性. 多任务学习通过利用跨疾病的共同遗传因素来提高多基因风险得分 (PRS).
科学领域:
- 遗传学 是一个遗传学.
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 复杂的疾病往往具有共同的遗传基础,并且在个体中一起发生.
- 提高复杂疾病的多基因风险评分 (PRS) 的准确性对于个性化医学至关重要.
研究的目的:
- 调查多任务学习 (MTL) 是否可以通过利用多种疾病的共同遗传决定因素来提高PRS的准确性.
- 测试假设,与单任务学习 (STL) 相比,同时进行PRS估计可以提高预测能力.
主要方法:
- 采用了使用可解释的神经网络架构的多任务学习 (MTL) 方法.
- 对17种流行癌症 (泛癌MTL模型) 和60种流行非癌症疾病 (泛疾病MTL模型) 进行并行估计PRS.
- 对单个疾病的独立单任务学习 (STL) 模型进行了性能比较.
主要成果:
- 泛癌MTL模型显示,与STL模型相比,单个癌症的PRS估计通常更准确.
- 在全疾病MTL模型中,由于积极的转移学习,观察到类似的性能改善.
- 解释揭示了MTL模型中使用的关键单核酸多态 (SNP) 之间的显著遗传相关性.
结论:
- 使用MTL同时进行PRS估计,有效地利用共享的遗传病因来提高多种疾病的预测准确性.
- 这些发现表明,疾病的网络连接得很好,遗传基础重叠.
- 在复杂疾病中,MTL提供了一种有前途的策略来提高多基因风险预测.
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