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BioKERN: Biological Kernel Regularization for Histology-to-Transcriptomics Neighborhood Retrieval — arXiv:2608.24823

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.24823
License (as archived): CC0-1.0
Archived: 2026-10-18T06:36:27.433Z
SHA-256 of the archived text: f00d70144d7bccfd070983d1bcacfbe2061c1cac16dd2ee8378507e6434b0c2b
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How to cite

BioKERN: Biological Kernel Regularization for Histology-to-Transcriptomics Neighborhood Retrieval — arXiv:2608.24823 — arXiv (en), https://arxiv.org/abs/2608.24823 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7a34aed55232 (Metraipolis archive dc_7a34aed55232, sha256 f00d70144d7bccfd070983d1bcacfbe2061c1cac16dd2ee8378507e6434b0c2b)

BibTeX

@misc{dc7a34aed55232,
  title = {BioKERN: Biological Kernel Regularization for Histology-to-Transcriptomics Neighborhood Retrieval — arXiv:2608.24823},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.24823},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7a34aed55232; sha256 f00d70144d7bccfd070983d1bcacfbe2061c1cac16dd2ee8378507e6434b0c2b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.24823. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-26 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG) ; Quantitative Methods (q-bio.QM).
Pagina del lavoro: https://arxiv.org/abs/2608.24823 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T06:28:52.286Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-26?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).