elonlit/GeneJEPA
https://huggingface.co/elonlit/GeneJEPAGeneJEPA is a Joint-Embedding Predictive Architecture (JEPA) trained for self-supervised representation learning on scRNA-seq. It uses a Perceiver-style encoder to handle sparse, high-dimensional gene count vectors and a Fourier-feature tokenizer for numerical tokenization.
Sourced from
- HuggingFace — elonlit/GeneJEPA
Related resources
hussenmi/scimilarity_expanded_model
by hussenmiAn extended version of SCimilarity, a metric-learning model for single-cell RNA-seq that maps cells to a unified 128-dimensional embedding space. The original model and method are described in:
minwoosun/uce-100m
by minwoosunUniversal Cell Embeddings (UCE) is a foundation model designed for single-cell RNA sequencing data analysis. UCE generates a universal representation of cells that captures the molecular diversity across different cell types, tissues, and species.
Xaira-Therapeutics/X-Cell
by Xaira-TherapeuticsA diffusion language model for genome-scale perturbation prediction across diverse cellular contexts.
FremyCompany/BioLORD-2023
by FremyCompany# FremyCompany/BioLORD-2023 This model was trained using BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts.
tahoebio/Tahoe-x1
by tahoebioTahoe-x1 is a family of perturbation-trained single-cell foundation models with up to 3 billion parameters, developed by Tahoe Therapeutics. Pretrained on 266 million single-cell transcriptomic profiles including the Tahoe-100M perturbation compendium, Tahoe-x1 achieves state-of-the-art performance…