All Competitors

Self-supervised single-cell foundation model that predicts masked gene embeddings in latent space using a joint-embedding predictive architecture.

Single-cell transcriptomics foundation model that encodes gene regulatory network structure into self-attention through graph signal processing.

Single-cell perturbation-response model that predicts transcriptomic and imaging outcomes of unseen genetic perturbations via a VAE with attention.

Virtual cell transformer that predicts how cells respond to genetic, chemical, or signaling perturbations, generalizing to unseen cellular contexts.

Single-cell perturbation prediction model that adds gene-level language embeddings from NCBI, UniProt, and Gene Ontology to scGPT representations.

Generative image perturbation autoencoder predicting cellular morphological responses to chemical and genetic perturbations from control images.

Perturbation prediction model that forecasts transcriptional responses to multi-gene CRISPR perturbations from scRNA-seq and a gene-gene graph.

Single-cell perturbation prediction model that forecasts transcriptional responses to drug combinations and doses never experimentally measured.