利用因果建模改进机器学习识别的生物标志物的泛化能力：一项关于免疫受体诊断的研究
Improving generalization of machine learning-identified biomarkers with causal modeling: an investigation into immune receptor diagnostics
AI总结：
该研究探讨了实验设计因素对机器学习诊断泛化能力的影响，以适应性免疫受体库（AIRRs）为例，通过模拟说明因果建模如何识别变量间稳定关系并指导调整人群间差异，从而提升生物标志物的鲁棒性。
摘要：
机器学习正越来越多地被用于从高维分子数据中发现诊断和预后生物标志物。然而，与实验设计相关的多种因素可能会影响学习具有泛化能力且适用于临床的诊断方法的能力。在此，我们认为因果视角有助于识别这些挑战，并形式化它们与基于机器学习的诊断的鲁棒性和泛化能力之间的关系。为了进行具体讨论，我们聚焦于一种特定的、最近建立的高维生物标志物——适应性免疫受体库（AIRRs）。通过模拟，我们阐明了AIRR领域的主要生物学和实验因素如何影响学习到的生物标志物。总之，我们认为因果建模通过识别变量间的稳定关系，并指导调整不同人群间变化的变量及其关系，从而提升了基于机器学习的生物标志物的鲁棒性。
英文摘要：
Machine learning is increasingly used to discover diagnostic and prognostic biomarkers from high-dimensional molecular data. However, a variety of factors related to experimental design may affect the ability to learn generalizable and clinically applicable diagnostics. Here, we argue that a causal perspective improves the identification of these challenges and formalizes their relation to the robustness and generalization of machine learning-based diagnostics. To make for a concrete discussion, we focus on a specific, recently established high-dimensional biomarker - adaptive immune receptor repertoires (AIRRs). Through simulations, we illustrate how major biological and experimental factors of the AIRR domain may influence the learned biomarkers. In conclusion, we argue that causal modeling improves machine learning-based biomarker robustness by identifying stable relations between variables and by guiding the adjustment of the relations and variables that vary between populations.
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