🚨🚨 AI in Bio release 🧬  Very happy to share my work on a Large Cell Model for Gene Network Inference. It is for now just a preprint and more is to come. We are asking the question: “What can 50M cells tell us about gene networks?” ❓Behind it, other questions arose like: “how can we best learn networks for scRNAseq data?”, “How would we assess them?” “What are foundation models in biology actually learning about the cell and its mechanism?” We try to partially answer these questions and present a new model: scPRINT 💫. 🏃 -> it is for now a pre-print and more is to come but here are some of our results: scPRINT is a transformer model trained on 50M cells 🦠 from the cellxgene database, it has novel expression encoding and decoding schemes and new pre-training methodologies 🤖. We propose to use the specificity of scRNAseq data and define a multi task pre-training composed of expression denoising, bottleneck learning and classification. We also propose a new hierarchical classification method to work with the rich hierarchical ontologies used to label cells in cellxgene. (1/2) xitter.cf/razoralign/status/1817…