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BioM-JEPA: joint-embedding prediction of graph-connected gene blocks in single cells

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Computer Science > Machine Learning

arXiv:2608.05928 (cs)
[Submitted on 6 Aug 2026]

Title:BioM-JEPA: joint-embedding prediction of graph-connected gene blocks in single cells

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Abstract:Single-cell transcriptomes are sparse observations of coordinated biological programmes, yet most self-supervised models learn by reconstructing individual genes. Here we present BioM-JEPA, a joint-embedding predictive architecture that instead predicts aggregate representations of graph-connected gene blocks defined by protein-association and corpus-derived coexpression evidence. A student network infers each target-block representation from the remaining genes in a cell, while a slowly updated teacher supplies the corresponding target from the full observed gene set. Under the reported extraction procedure, block-level prediction produced embeddings with higher effective rank and weaker association with detected-gene depth in the tested diagnostics than token-prediction, random-block and reconstruction controls. Across CellBench tasks, frozen BioM-JEPA embeddings retained expression, pathway and neighbourhood information and achieved the lowest aggregate perturbation-response error among the evaluated models. Representation diagnostics were also consistent with canonical pancreatic programmes and compositional relationships between genetic perturbations. Linear attention avoids constructing a quadratic gene-by-gene attention matrix; in a matched one-epoch hPancreas experiment at batch size 8, BioM-JEPA provided 5.75-fold higher fine-tuning throughput and 3.76-fold higher held-out embedding throughput than scFoundation. Together, these results support graph-connected gene blocks as useful prediction units for JEPA-style representation learning in single-cell biology.
Comments: 34 pages, 6 figures, and 13 supplementary tables (Tables S1-S13); includes Supplementary Information with detailed training and evaluation protocols. Numerical source data for all figures are provided as ancillary files; training code and the BioM-JEPA checkpoint will be released via GitHub
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2608.05928 [cs.LG]
  (or arXiv:2608.05928v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2608.05928
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Zelin Zang [view email]
[v1] Thu, 6 Aug 2026 11:58:29 UTC (4,510 KB)
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