BABEL

totalVI

Deep probabilistic analysis of single-cell and spatial omics data

scGPT

scGPT is a foundation model designed for single-cell multi-omics data analysis using generative AI. It leverages transformer architecture pretrained on millions of single-cell profiles to enable a wide range of downstream biological tasks, advancing computational biology by providing a powerful, unified model for cellular data. ## Key Features - **Pretrained Model Zoo** — Offers multiple organ-specific and whole-human models trained on millions of cells for various applications. - **Zero-Shot Applications** — Supports tasks like cell embedding and reference mapping without task-specific training. - **Reference Mapping** — Enables fast similarity search across millions of cells using efficient indexing with faiss. - **Multi-Task Fine-Tuning** — Can be adapted for scRNA-seq integration, cell type annotation, perturbation prediction, and GRN inference. - **Online Tools** — Provides accessible web applications for reference mapping, cell annotation, and GRN inference via cloud GPUs. ## Philosophy scGPT aims to build a foundational AI model for single-cell biology, democratizing access to advanced computational methods and accelerating discoveries in multi-omics research through open-source collaboration.

UNI

Pathology Foundation Model - Nature Medicine

GigaPath

Prov-GigaPath: A whole-slide foundation model for digital pathology from real-world data