Integrating gene regulatory priors into Transformer attention with scTransformer for interpretable scRNA-seq analysis
Motivation: Transformer-based models are increasingly applied to large-scale single-cell transcriptomics, showing strong performance through self-supervised learning on millions of cells. However, most existing approaches treat genes as independent features, and largely ignore prior biological knowledge, which limits interpretability and robustness. In this paper, we explore whether explicitly incorporating gene regulatory information can improve both model performance and biological insight. Results: We present scTransformer, the first Transformer-based approach that builds a priori knowledge of biological mechanisms into the model's attention patterns. By constraining information flow a...
| Authors | Mikele Milia, Louis Fabrice Tshimanga, Henning Mueller et al. |
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| Primary category | q-bio.GN |
| Published | 8 June 2026 |
| Last updated at source | 8 June 2026 |
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