The paper presents an overview of
Tahoe-x1 (Tx1), a new family of perturbation-trained single-cell foundation models developed by Tahoe Therapeutics, with up to three billion parameters. The authors explain that foundation models, which have transformed fields like computer vision, are now being adapted for single-cell biology, particularly for applications in
precision oncology. Tx1 is pretrained on massive datasets, including the
Tahoe-100M perturbation compendium, to learn generalized representations of genes, cells, and compounds. The model achieves
state-of-the-art performance across multiple cancer-relevant benchmarks, such as predicting
gene essentiality and modeling cellular responses to various
chemical perturbations. Ultimately, the research emphasizes that
scaling model capacity and training on
large-scale perturbation data are crucial for developing robust, transferable models for cancer therapeutics.
References:
- Gandhi S, Javadi F, Svensson V, et al. Tahoe-x1: Scaling Perturbation-Trained Single-Cell Foundation Models to 3 Billion Parameters[J]. bioRxiv, 2025: 2025.10. 23.683759.