不确定性引导的表格基础模型在生物分子功效预测中的模型选择
Uncertainty-Guided Model Selection for Tabular Foundation Models in Biomolecule Efficacy Prediction
AI总结：
本研究提出不确定性引导的模型选择策略，利用TabPFN的预测区间四分位距作为无标签启发式，通过OligoICP方法选择低不确定性模型集成，提升siRNA功效预测性能。
摘要：
像TabPFN这样的上下文学习器在生物分子功效预测中具有前景，其中已建立的分子特征集和相关实验结果可以作为强大的上下文示例。然而，它们的性能对所提供的上下文高度敏感，这使得对在不同数据子集上训练的模型进行事后集成等策略成为一种可行的方法。一个开放的问题是如何在没有真实标签的情况下选择最佳的集成模型。在本研究中，我们探讨了一种不确定性引导的模型选择策略。我们在siRNA敲低功效任务上证明，使用简单基于序列特征的TabPFN模型可以超越专门的最先进预测器。我们还表明，该模型预测的区间四分位距（IQR），作为其不确定性的度量，与真实预测误差呈负相关。我们开发了OligoICP方法，该方法选择并平均具有最低平均IQR的模型集成用于siRNA功效预测，与朴素集成或使用在全部可用数据上训练的单一模型相比，实现了更优的性能。这一发现强调了模型不确定性作为一种强大的、无标签的启发式方法，用于优化生物分子功效预测。
英文摘要：
In-context learners like TabPFN are promising for biomolecule efficacy prediction, where established molecular feature sets and relevant experimental results can serve as powerful contextual examples. However, their performance is highly sensitive to the provided context, making strategies like post-hoc ensembling of models trained on different data subsets a viable approach. An open question is how to select the best models for the ensemble without access to ground truth labels. In this study, we investigate an uncertainty-guided strategy for model selection. We demonstrate on an siRNA knockdown efficacy task that a TabPFN model using straightforward sequence-based features can surpass specialized state-of-the-art predictors. We also show that the model's predicted inter-quantile range (IQR), a measure of its uncertainty, has a negative correlation with true prediction error. We developed the OligoICP method, which selects and averages an ensemble of models with the lowest mean IQR for siRNA efficacy prediction, achieving superior performance compared to naive ensembling or using a single model trained on all available data. This finding highlights model uncertainty as a powerful, label-free heuristic for optimizing biomolecule efficacy predictions.
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