GeneICL: A Tabular Foundation Model for Bulk Transcriptomics
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.
ProofPaper ↗
Key points
- 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.
- 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.
Sources (1)
- [1]GeneICL: A Tabular Foundation Model for Bulk TranscriptomicsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 05:10 PM
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.
Gene expression is widely measured in biomedicine, yet clinical outcome prediction remains challenging due to high dimensionality, strong feature correlations, and limited labeled data.
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