建模基因表达分布变化，提升药物靶点发现的准确性
Modeling Gene Expression Distributional Shifts for Unseen Genetic Perturbations
- 用神经网络预测基因表达的完整分布，而非仅均值
- 在低训练成本下更好捕捉方差、偏度等高阶统计特征
- 融合大语言模型基因嵌入，可推广至未见扰动场景
我们训练了一个神经网络，用于预测遗传扰动后基因表达的分布变化。这一任务在新药研发早期至关重要，因表达响应能揭示基因功能并辅助靶点识别。现有方法仅预测表达均值的变化，忽略了单细胞数据中的随机性。相比之下，本工作通过建模表达分布，提供了更真实的细胞反应图景。模型以扰动为条件，预测基因层面的直方图，在训练成本仅为基线几分之一的情况下，显著优于基线对方差、偏度和峰度等高阶统计量的捕捉能力。为实现对未见扰动的泛化，我们引入大语言模型（LLM）生成的基因嵌入作为先验知识。尽管输出空间更丰富，该方法在预测均值变化方面仍保持竞争力。这项工作推动了更具表现力和生物学意义的扰动效应建模。
原文摘要 · Abstract (English)
We train a neural network to predict distributional responses in gene expression following genetic perturbations. This is an essential task in early-stage drug discovery, where such responses can offer insights into gene function and inform target identification. Existing methods only predict changes in the mean expression, overlooking stochasticity inherent in single-cell data. In contrast, we offer a more realistic view of cellular responses by modeling expression distributions. Our model predicts gene-level histograms conditioned on perturbations and outperforms baselines in capturing higher-order statistics, such as variance, skewness, and kurtosis, at a fraction of the training cost. To generalize to unseen perturbations, we incorporate prior knowledge via gene embeddings from large language models (LLMs). While modeling a richer output space, the method remains competitive in predicting mean expression changes. This work offers a practical step towards more expressive and biologically informative models of perturbation effects.
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