DA-ICL：用分布感知上下文学习建模癌症预测的遗传祖先连续谱
Beyond Discrete Ancestry Groups: Distribution-Aware In-Context Learning for Cancer Outcome Prediction Across the Genetic Ancestry Continuum
做生医机器学习的人该看看这篇：它指出了按祖先分组建模的真实缺陷，即遗传距离连续变化导致精度下滑，然后用上下文学习基础模型改成连续谱建模，还给了理论证明和开源代码，方法可以直接搬到自己的组学预测任务里试。
一篇 arXiv 论文提出 DA-ICL（distribution-aware in-context learning），用于基于组学数据的癌症结局预测，将传统按离散祖先分组建模的方式改为遗传连续谱建模。
作者指出，即便在同一祖先组内部，预测精度也会随样本间遗传距离增加而下降，因为数据分布是连续变化的，不能只用预定义类别划分。该方法借助上下文学习基础模型，捕捉沿遗传连续谱非平稳的组学-终点关系，并通过理论分析论证连续谱建模优于离散建模。
论文配套开源了代码，仓库地址为 github.com/ai4pm/DA-ICL-Continuum，可直接复现实验。
Beyond Discrete Ancestry Groups: Distribution-Aware In-Context Learning for Cancer Outcome Prediction Across the Genetic Ancestry Continuum
Machine learning models for omics-based prediction of cancer outcomes are commonly built on aggregated data without distinguishing ancestry groups, or under an ancestry-discrete design, which partitions samples into ancestry groups and fits a separate model within each group. Recent evidence shows that this approach is inherently limited: predictive accuracy declines with genetic distance from the training samples, even within a single ancestry group, because the data distribution shifts continuously rather than only between predefined categories. In this paper, we propose distribution-aware in-context learning (DA-ICL) for ancestry-continuum modeling. Our method extends conventional ancestry-discrete modeling to ancestry-continuum modeling, capturing the nonstationary omics-endpoint relationship across the genetic continuum while enabling efficient modeling through an in-context learning foundation model. We provide theoretical analysis demonstrating the superiority of the ancestry-continuum over ancestry-discrete modeling, along with empirical results supporting the proposed framework. Source code is available at https://github.com/ai4pm/DA-ICL-Continuum.
本条情报归属 AITOP「论文」核心追踪栏目。涉及核心实体与技术议题包括：癌症预测、上下文学习、组学数据、DA-ICL，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始线索采编自「arXiv cs.LG」（@Weizhi Li, Yan Cui），经 AITOP 多维评分管线结构化提炼。
