Towards foundation models that learn across biological scales, KALFON, Jérémie, Cantini, Laura, Peyre, Gabriel

Une clarification de mon intervention sur France Inter, en français et en anglais.

Send and read messages on Facebook Messenger, WhatsApp, Instagram, LinkedIn, Twitter/X, Signal, Telegram, Discord and other networks through a Beeper account...

bioRxiv - the preprint server for biology, operated by openRxiv, a nonprofit organization dedicated to advancing scientific communication

The 1st Open Conference of AI Agents for Science (agents4science 2025). AI serves as both primary authors and reviewers of research papers.

Made with Softr, the easiest way to turn your data into portals and internal tools.

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

Join Superbio and the researchers behind scPRINT, a large transformer model trained on over 50 million single cells. Designed to infer gene networks without supervision, scPRINT outperforms existing tools and shows strong performance in denoising, batch effect correction, and cell type prediction. We’ll explore how this model was built, how it works, and what it reveals about aging and inflammation in prostate tissue. Perfect for researchers in single-cell biology, computational biology, and AI in life sciences.

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...

📢 Webinar alert: Mapping gene networks in single cells with AI Join Superbio and Jeremie Kalfon, the researcher behind, scPRINT for a live webinar on a new AI model trained on over 50 million single cells. scPRINT infers gene networks without supervision and uncovers complex biology at single-cell resolution. Register now: https://lnkd.in/gfe8QpTg We’ll cover: ✅ How scPRINT uses transformers to analyze large-scale single-cell data ✅ Its performance in gene network inference, denoising, and batch correction ✅ What it reveals about aging and inflammation in prostate tissue

Spring School on AI and Machine Learning and their Applications to Biology and Health -  Université Lyon 1, La Doua, Villeurbanne, April 7-9 2025.Artificial intelligence and machine learning are rapidly transforming our research activity in biological sciences and health. As a result, there is a critical need for making machine learning and AI accessible to everyone currently working in these scientific and applied domains. To meet those challenges, the DIgitBioMed initiative proposes a...