用贝叶斯方法同时评估基因扰动预测的可靠性，提升结果可信度。
PRESCRIBE: Predicting Single-Cell Responses with Bayesian Estimation
- 基于多变量深度证据回归，联合建模基因相似性和数据质量不确定性
- 预测置信度与实际准确率强相关，过滤不可靠结果后准确率提升超3%
- 适合需要高可靠性的单细胞基因功能研究者使用
在单细胞扰动预测中，核心任务是预测训练数据中未见基因的扰动效应。预测效果受两个因素影响：(1) 目标基因与训练数据中基因的相似性，反映模型（认知）不确定性；(2) 对应训练数据的质量，体现数据（随机）不确定性。两者对判断预测可靠性至关重要，尤其因基因扰动本身具有内在随机性。本文提出PRESCRIBE（PREdicting Single-Cell Response wIth Bayesian Estimation），一种多变量深度证据回归框架，用于联合衡量两种不确定性。分析表明，PRESCRIBE能有效生成每个预测的置信度分数，该分数与实际准确率高度相关。此能力使不可信结果得以过滤，在实验中相较基线实现超过3%的稳定准确率提升。
原文摘要 · Abstract (English)
In single-cell perturbation prediction, a central task is to forecast the effects of perturbing a gene unseen in the training data. The efficacy of such predictions depends on two factors: (1) the similarity of the target gene to those covered in the training data, which informs model (epistemic) uncertainty, and (2) the quality of the corresponding training data, which reflects data (aleatoric) uncertainty. Both factors are critical for determining the reliability of a prediction, particularly as gene perturbation is an inherently stochastic biochemical process. In this paper, we propose PRESCRIBE (PREdicting Single-Cell Response wIth Bayesian Estimation), a multivariate deep evidential regression framework designed to measure both sources of uncertainty jointly. Our analysis demonstrates that PRESCRIBE effectively estimates a confidence score for each prediction, which strongly correlates with its empirical accuracy. This capability enables the filtering of untrustworthy results, and in our experiments, it achieves steady accuracy improvements of over 3% compared to comparable baselines.
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