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This research challenges the assumption that massive, atlas-scale datasets are necessary for effective transcriptomic foundation models by demonstrating that a carefully curated, smaller corpus yields superior performance. The authors introduce TxFM, a masked autoencoding architecture specifically designed to handle technical noise and batch effects inherent in RNA sequencing data without relying on deep scaling laws. These findings offer a more practical pathway for applying self-supervised learning to biological discovery where data volume is constrained but quality is paramount.
Effective Biological Representation Learning by Masking Gene Expression
arXiv:2605.31562v2 Announce Type: replace Abstract: RNA sequencing produces rich and diverse datasets of gene expression, offering compelling insights into cellular state and function that have many applications in drug discovery. Modeling such data is challenging due to inherent technical noise and experimental batch effects, as evidenced by many existing transcriptomic foundation models (FMs) underperforming relative to linear baselines. Such results raise the question of whether deep representation learning provides a distinct advantage over the direct use of raw transcript counts. Our work explores this by developing a new self-supervised model, TxFM, with a focus on inductive representation learning evaluations. TxFM employs a masked autoencoding approach tailored to diverse RNA-seq count data, and our ablation study empirically identifies crucial architecture configurations required for strong transfer performance. Additionally, we curate a public training corpus, DiverseRNA-1.4M, and find that TxFM trained on this curated dataset yields high-fidelity gene representations that outperform FMs trained on atlas-scale corpora over 100x larger. Overall, our results indicate that inductive self-supervised learning is a viable modeling approach for transcriptomics representation, provided a careful synthesis of model architecture and training data curation.
Abstract:RNA sequencing produces rich and diverse datasets of gene expression, offering compelling insights into cellular state and function that have many applications in drug discovery. Modeling such data is challenging due to inherent technical noise and experimental batch effects, as evidenced by many existing transcriptomic foundation models (FMs) underperforming relative to linear baselines. Such results raise the question of whether deep representation learning provides a distinct advantage over the direct use of raw transcript counts. Our work explores this by developing a new self-supervised model, TxFM, with a focus on inductive representation learning evaluations. TxFM employs a masked autoencoding approach tailored to diverse RNA-seq count data, and our ablation study empirically identifies crucial architecture configurations required for strong transfer performance. Additionally, we curate a public training corpus, DiverseRNA-1.4M, and find that TxFM trained on this curated dataset yields high-fidelity gene representations that outperform FMs trained on atlas-scale corpora over 100x larger. Overall, our results indicate that inductive self-supervised learning is a viable modeling approach for transcriptomics representation, provided a careful synthesis of model architecture and training data curation.
31 pages, 11 figures. Accepted for main-track at the 40th Conference on Neural Information Processing Systems (NeurIPS 2026). Also presented at ICLR 2026 2nd Workshop on Foundation Models for Science: Real-World Impact and Science-First Design
Machine Learning (cs.LG)
arXiv:2605.31562 [cs.LG]
(or arXiv:2605.31562v2 [cs.LG] for this version)
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