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Nature Communications - Authors present a state-of-the-art cell foundation model trained on 50 million cells. They show that the model generates a meaningful gene network and has zero-shot...

scPRINT is a cell foundation model, also called a Large Cell Model (LCM), trained on single-cell RNA sequence (scRNAseq) data from more than 50M human and mouse cells available through CZ CELLxGENE. Based on the transformer architecture, the model is fully open source and reproducible, with multiple checkpoint sizes available from 2M to 100M parameters. scPRINT demonstrated high performance for genome-wide cell-specific gene network inference when benchmarked against state-of-the-art models (e.g., scGPT, Geneformer v2, GENIE3). In addition, scPRINT has various zero-shot capabilities, including cell embedding, cell label prediction (e.g., cell type, sex, disease), and gene expression imputation, highlighting its potential as a versatile tool for single-cell analysis.

TL;DR In this talk, Jérémie Kalfon presents his paper “scPRINT: pre-training on 50 million cells allows robust gene network predictions” at the Foundation Models for Biology Seminar Series by GenBio AI. He introduces scPRINT, a transformer-based foundation model trained on over 50 million single-cell RNA-seq profiles to infer gene networks. scPRINT enables scientists to predict

Modern single-cell datasets now comprise hundreds of millions of cells, presenting significant challenges for training deep learning models that require shuffled, memory-efficient data loading....

Models, Inference and Algorithms March 11, 2025 Broad Institute of MIT and Harvard Primer: Heterogeneous reconstruction in cryo-EM Rishwanth Raghu Princeton University Department of Computer Science Meeting: Machine learning for visualizing structural landscapes inside the cell Ellen Zhong Princ