用图注意力网络建模基因通路，提升预测准确性并发现新生物机制。
Biological Pathway Informed Models with Graph Attention Networks (GATs)
- 用GAT直接建模基因间拓扑关系，突破传统袋式处理局限。
- 在未见治疗条件下预测通路动态，均方误差降低81%。
- 能从原始数据中复现经典TP53反馈环，适合生物发现研究。
生物通路描绘了调控人类所有生理过程的基因间相互作用。尽管重要，多数机器学习模型仍将基因视为无结构标记，忽略已知通路结构。最新通路感知模型虽捕捉通路间交互，但仍以MLP将每个通路视为“基因袋”，忽视其拓扑结构与基因间互作。本文提出基于图注意力网络（GAT）的框架，在基因层面建模通路。实验表明，相较于MLP，GAT在未见治疗条件下预测通路动态时，均方误差（MSE）降低81%。进一步通过边干预编码药物机制，验证了生物先验的有效性，提升了模型鲁棒性。最后，我们的GAT模型成功从原始时间序列mRNA数据中复现了经典TP53-MDM2-MDM4反馈回路中的全部五个基因-基因互作，展示了直接从实验数据生成新生物学假说的潜力。
原文摘要 · Abstract (English)
Biological pathways map gene-gene interactions that govern all human processes. Despite their importance, most ML models treat genes as unstructured tokens, discarding known pathway structure. The latest pathway-informed models capture pathway-pathway interactions, but still treat each pathway as a "bag of genes" via MLPs, discarding its topology and gene-gene interactions. We propose a Graph Attention Network (GAT) framework that models pathways at the gene level. We show that GATs generalize much better than MLPs, achieving an 81% reduction in MSE when predicting pathway dynamics under unseen treatment conditions. We further validate the correctness of our biological prior by encoding drug mechanisms via edge interventions, boosting model robustness. Finally, we show that our GAT model is able to correctly rediscover all five gene-gene interactions in the canonical TP53-MDM2-MDM4 feedback loop from raw time-series mRNA data, demonstrating potential to generate novel biological hypotheses directly from experimental data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。