BioKERN: Biological Kernel Regularization for Histology-to-Transcriptomics Neighborhood Retrieval
cs.LG, q-bio.QM
Submitted: 2026-08-25
Updated: 2026-08-25
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
The gist: Spatially resolved biology requires representations that preserve biological neighborhood structure rather than only exact cross-modal correspondences.
Terminology
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.
Sources
- A Large-Scale Benchmark of Cross-Modal Learning for Histology and Gene Expression in Spatial Transcriptomics
- 1953: Fermi's "little discovery" and the birth of the numerical experiment
- HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis
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