利用基因关系图谱预测未知基因扰动的细胞反应，提升治疗设计准确性。
TxPert: Leveraging Biochemical Relationships for Out-of-Distribution Transcriptomic Perturbation Prediction
- 融合多源基因关系知识图谱，增强模型对未知扰动的泛化能力
- 在未见单/双基因扰动及细胞系上实现领先性能，验证跨场景适用性
- 适用于药物研发中复杂基因扰动的预测，尤其适合生物医学研究者
准确预测基因扰动引起的细胞响应对于理解疾病机制和设计有效疗法至关重要。然而，全面探索可能的扰动（如多基因扰动或跨组织、细胞类型）成本过高，亟需可泛化至未见条件的方法。本文探讨如何利用基因-基因关系的知识图谱，提升三种挑战性场景下的分布外（OOD）预测表现：未见单基因扰动、未见双基因扰动以及未见细胞系。我们提出：(i) TxPert，一种新方法，通过整合多个生物学知识网络，在分布外条件下预测转录响应；(ii) 深入分析图谱、模型结构与数据对性能的影响；(iii) 构建扩展的基准评估框架，强化扰动建模的评测标准。
原文摘要 · Abstract (English)
Accurately predicting cellular responses to genetic perturbations is essential for understanding disease mechanisms and designing effective therapies. Yet exhaustively exploring the space of possible perturbations (e.g., multi-gene perturbations or across tissues and cell types) is prohibitively expensive, motivating methods that can generalize to unseen conditions. In this work, we explore how knowledge graphs of gene-gene relationships can improve out-of-distribution (OOD) prediction across three challenging settings: unseen single perturbations; unseen double perturbations; and unseen cell lines. In particular, we present: (i) TxPert, a new state-of-the-art method that leverages multiple biological knowledge networks to predict transcriptional responses under OOD scenarios; (ii) an in-depth analysis demonstrating the impact of graphs, model architecture, and data on performance; and (iii) an expanded benchmarking framework that strengthens evaluation standards for perturbation modeling.
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