BioKERN: Biological Kernel Regularization for Histology-to-Transcriptomics Neighborhood Retrieval

Abstract: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.

Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)

arXiv:2608.24823 [cs.LG]

(or arXiv:2608.24823v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.24823 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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