用混合潜流模型统一预测多种癌症的基因表达，提升泛化能力。
MoLF: Mixture-of-Latent-Flow for Pan-Cancer Spatial Gene Expression Prediction from Histology
- 采用混合专家潜流架构，动态分配不同癌种数据到专用子网络。
- 在跨癌种基准上超越现有方法，零样本迁移至跨物种数据表现优异。
- 适合需要泛化能力强的癌症空间转录组研究者使用。
从组织学图像推断空间转录组（ST）可实现可扩展的组织基因组分析，但现有方法多局限于单组织模型。这种分割状态无法利用跨癌种共享的生物学规律，限制了数据稀缺场景的应用。尽管跨癌种训练是解决方案，但由此带来的异质性挑战了单一架构的优化。为此，我们提出MoLF（Mixture-of-Latent-Flow），一种用于跨癌种组织基因组预测的生成模型。MoLF采用条件流匹配目标，将噪声映射到基因潜空间，其速度场由混合专家（MoE）参数化。通过动态路由输入至专用子网络，该架构有效解耦了多样组织模式的优化过程。实验表明，MoLF在跨癌种基准上建立新最优性能，持续优于专用模型与基础模型基线。此外，其在跨物种数据上展现零样本泛化能力，表明其捕捉到了基本且保守的组织-分子机制。
原文摘要 · Abstract (English)
Inferring spatial transcriptomics (ST) from histology enables scalable histogenomic profiling, yet current methods are largely restricted to single-tissue models. This fragmentation fails to leverage biological principles shared across cancer types and hinders application to data-scarce scenarios. While pan-cancer training offers a solution, the resulting heterogeneity challenges monolithic architectures. To bridge this gap, we introduce MoLF (Mixture-of-Latent-Flow), a generative model for pan-cancer histogenomic prediction. MoLF leverages a conditional Flow Matching objective to map noise to the gene latent manifold, parameterized by a Mixture-of-Experts (MoE) velocity field. By dynamically routing inputs to specialized sub-networks, this architecture effectively decouples the optimization of diverse tissue patterns. Our experiments demonstrate that MoLF establishes a new state-of-the-art, consistently outperforming both specialized and foundation model baselines on pan-cancer benchmarks. Furthermore, MoLF exhibits zero-shot generalization to cross-species data, suggesting it captures fundamental, conserved histo-molecular mechanisms.
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