提出新方法评估自适应采样能否替代固定实验计划
Testing when adaptive data acquisition can replace fixed measurement plans
- 引入机会感知协议，先学规则再验证，避免结果偏差
- 在1.1万化合物上测试，最优规则误选率低于3%，减少不必要的测量
- 首次区分预测价值与决策依据，适合实验设计优化者
在高通量实验中，学习得到的规则用于选择后续测量样本。仅凭预测值不足以证明可替代固定测量方案。本文提出机会感知协议（Opal），从标注数据中学习规则，在结果开放前固定规则，并在保留样本上测试。当结果存在不受限的偏移时，其他来源或未标注目标测量无法做出此决策。一个精确边界识别了过小的试点试验，无法对未测量单元做出结论。在11,265个保留的Cell Painting化合物上，最高价值规则选择了96.0%进行额外成像，误选的上界为97.1%。Opal选择5.28%，将该上界降至5.18%，并考虑成本后仍保持正收益。只有Opal满足注册的假激活限制。次低上界为37.94%。被选化合物中的错误不确定性仍高于目标水平。Opal成功将预测价值与取代固定方案的证据分开。
原文摘要 · Abstract (English)
Learned rules select samples for follow-up measurements in high-throughput experiments. Predicted value does not justify replacing a fixed plan. We introduce the opportunity-aware protocol for authorizing learned measurement rules (Opal), which learns a rule from labelled data, fixes it before outcomes are opened and tests it on held-out samples. Outcomes from elsewhere and unlabelled target measurements cannot settle this decision under unrestricted outcome shift. An exact bound identifies pilots too small for claims about unmeasured units. On 11,265 held-out Cell Painting compounds, the highest-value rule selected 96.0% for additional imaging, with a 97.1% upper bound on unnecessary selections. Opal selected 5.28%, reduced this bound to 5.18% and retained positive value after cost. Only Opal met the registered false-activation limit among rules selecting compounds. The next-lowest upper bound was 37.94%. Uncertainty about errors among selected compounds remained above target. Opal separates predicted value from evidence sufficient to replace the fixed plan.
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