GeneICL: A Tabular Foundation Model for Bulk Transcriptomics

Michael Bohl; Alexander Theus; David Wissel; Valentina Boeva

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arXiv · 2610.08694

GeneICL: A Tabular Foundation Model for Bulk Transcriptomics

Michael Bohl·Alexander Theus·David Wissel·Valentina Boeva

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Abstract

Gene expression is widely measured in biomedicine, yet clinical outcome prediction remains challenging due to high dimensionality, strong feature correlations, and limited labeled data. Large self-supervised transcriptomic foundation models often fail to outperform simple supervised baselines. Tabular foundation models offer an alternative through in-context learning, but are typically pretrained on generic synthetic data rather than transcriptomic structure. We ask whether transcriptomics-aware pretraining, rather than scale, is the missing ingredient. Towards this end, we introduce GeneICL, a 4.2M-parameter tabular foundation model combining a semi-synthetic pretraining prior built from measured bulk expression profiles with a parameter-efficient recurrent architecture. We further enable right-censored survival prediction via a training-free reduction to regression using Cox partial-likelihood residuals. We evaluate GeneICL on 80 clinical outcome-prediction tasks spanning classification, regression, and survival. Tabular foundation models consistently outperform self-supervised transcriptomic models, while GeneICL achieves the best overall rank among evaluated foundation models and tuned baselines. GeneICL does so with up to 387$\times$ fewer parameters, no gradient updates at inference, and predictions within seconds on a laptop CPU.

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BibTeX RIS

Michael Bohl, Alexander Theus, David Wissel, Valentina Boeva. 2026-10-06. GeneICL: A Tabular Foundation Model for Bulk Transcriptomics. https://arxiv.org/abs/2610.08694

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