[2503.03485] TEDDY: A Family Of Foundation Models For Understanding Single Cell Biology

Computer Science > Machine Learning arXiv:2503.03485 (cs) [Submitted on 5 Mar 2025 (v1), last revised 2 Apr 2026 (this version, v2)] Title:TEDDY: A Family Of Foundation Models For Understanding Single Cell Biology Authors:Alexis Chevalier, Soumya Ghosh, Urvi Awasthi, James Watkins, Julia Bieniewska, Nichita Mitrea, Olga Kotova, Kirill Shkura, Andrew Noble, Michael Steinbaugh, Vijay Sadashivaiah, George Dasoulas, Julien Delile, Christoph Meier, Leonid Zhukov, Iya Khalil, Srayanta Mukherjee, Judith Mueller View a PDF of the paper titled TEDDY: A Family Of Foundation Models For Understanding Single Cell Biology, by Alexis Chevalier and 17 other authors View PDF HTML (experimental) Abstract:Understanding the biological mechanisms of disease is crucial for medicine, and in particular, for drug discovery. AI-powered analysis of genome-scale biological data holds great potential in this regard. The increasing availability of single-cell RNA sequencing data has enabled the development of large foundation models for disease biology. However, existing foundation models only modestly improve over task-specific models in downstream applications. Here, we explored two avenues for improving single-cell foundation models. First, we scaled the pre-training data to a diverse collection of 116 million cells, which is larger than those used by previous models. Second, we leveraged the availability of large-scale biological annotations as a form of supervision during pre-training. We trained ...