BioKERN：用于组织学-转录组学邻域检索的生物核正则化方法

中文摘要 AI 辅助

空间解析生物学需要能够保留生物邻域结构的表示，而非仅保留精确的跨模态对应关系。现有的组织学-转录组学目标函数可能会强调实例级匹配，即使未配对的斑点共享分子或空间上下文。我们提出BioKERN，这是一种多模态空间表示学习框架，将生物结构作为显式可学习的归纳偏置纳入其中。BioKERN在训练时通过结合转录组相似性和空间邻近性构建生物核，随后用其提供分级邻域监督并正则化嵌入几何。评估采用所有方法共享的固定、与模型无关的生物邻域定义。在Mouse Brain Visium和Human Liver GSE240429数据集上，BioKERN在单尺度和多尺度设置下均比BLEEP持续提升生物邻域检索性能。控制共享架构实验显示，大部分提升源于生物核正则化而非模型容量的增加。这些结果表明，显式生物几何可作为空间生物学多模态学习的可解释归纳偏置。

英文摘要

Spatially resolved biology requires representations that preserve biological neighborhood structure rather than only exact cross-modal correspondences. Existing histology--transcriptomics objectives can emphasize instance-level matching even when non-paired spots share molecular or spatial context. We introduce BioKERN, a multimodal spatial representation-learning framework that incorporates biological structure as an explicit, learnable inductive bias. BioKERN constructs a training-time biological kernel by combining transcriptomic similarity and spatial proximity, then uses it to provide graded neighborhood supervision and regularize embedding geometry. Evaluation uses a fixed, model-independent biological neighborhood definition shared by all methods. Across Mouse Brain Visium and Human Liver GSE240429, BioKERN consistently improves biological-neighborhood retrieval over BLEEP in both single- and multi-scale settings. Controlled shared-architecture experiments show that most of the improvement arises from biological-kernel regularization rather than increased model capacity. These results support explicit biological geometry as an interpretable inductive bias for multimodal learning in spatial biology.

发表机构

• Rice University（莱斯大学）

机构由 AI 辅助整理，请以论文原文为准。