arXiv Machine Learning June 9, 2026 By Mikele Milia, Louis Fabrice Tshimanga, Henning Mueller, Manfredo Atzori, Barbara Di Camillo

Integrating gene regulatory priors into Transformer attention with scTransformer for interpretable scRNA-seq analysis

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Related stories

Prior-Guided Multi-Omic Transformers for Single-Cell Gene Regulatory Network Inference

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Beyond Gene Reconstruction: Learning Cell Representations through Complementary Transcriptomic Views

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