Integrating gene regulatory priors into Transformer attention with scTransformer for interpretable scRNA-seq analysis
arXiv:2606.09558v1 Announce Type: cross Abstract: 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 Transfo
The increasing scale and application of Transformer-based models in biological analysis necessitates methods to enhance interpretability and integrate existing biological knowledge.
This development improves deep learning's ability to model complex biological systems, reducing the 'black box' problem and offering more actionable insights for drug discovery and synthetic biology.
AI models for genomics can now incorporate prior biological information directly, leading to more robust, interpretable predictions and accelerated scientific discovery.
- · Bio-pharmaceutical companies
- · Genomic research institutions
- · AI/ML developers in biotech
- · Synthetic biology companies
- · Traditional statistical genetic methods
- · AI models lacking biological interpretability
Improved accuracy and interpretability of AI models in single-cell genomics.
Faster discovery of novel drug targets and therapeutic mechanisms as AI integrates more biological context.
Acceleration of personalized medicine and synthetic biology applications due to deeper understanding of gene regulation.
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Read at arXiv cs.LG