prov-gigatime/GigaTIME

GeneJEPA 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.

# MMedS-Llama3 💻Github Repo 🖨️arXiv Paper

Universal 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.

This is a ReactionT5 pre-trained to predict the products of reactions.

A diffusion language model for genome-scale perturbation prediction across diverse cellular contexts.

An 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: