BioKERN结合了转录组相似性和空间邻近性，有效提升了生物邻域检索的准确性，值得研究空间生物学领域的专业人士关注。
BioKERN是一种多模态空间表示学习框架，通过结合转录组相似性和空间邻近性构建生物核，用于提供分级邻域监督和正则化嵌入几何。在单尺度和多尺度设置中，BioKERN在鼠标大脑Visium和人类肝脏GSE240429数据集上均优于BLEEP。实验表明，大部分改进来自生物核正则化而非模型容量增加。
BioKERN: Biological Kernel Regularization for Histology-to-Transcriptomics Neighborhood Retrieval
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.