All Competitors

Single-cell foundation model trained by metric learning to embed scRNA-seq profiles for cell type annotation and similarity search in cell atlases.

Patient-level foundation model that pools every cell in an scRNA-seq sample into one disease representation, trained on 24.3 million cells.

Single-cell foundation model that compresses each expression profile into 64 cross-attention patch tokens for annotation and spatial transfer.

Single-cell foundation model that maps new scRNA-seq datasets onto a metacell coordinate system zero-shot, without batch correction or fine-tuning.

Single-cell transcriptomic aging clock predicting immune age for CD8+, CD4+ T and NK cells, and transferring to bulk whole-blood RNA-seq.

Cell-type-specific gene expression prediction from DNA sequence, mapping Enformer epigenomic features to pseudobulk expression for cell-resolved TWAS.

Patient-level single-cell foundation model that condenses a donor's scRNA-seq profile into one 288-dimensional embedding for disease cohort search.

Single-cell RNA-seq foundation model pretrained only on malignant cells, for zero-shot batch integration and drug response prediction in tumors.

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

Multi-modal, multi-task biological foundation model trained on 2 billion samples spanning proteins, small molecules, and single-cell gene expression.

Single-cell model that ranks the genes driving a cell state transition, using a gene graph-enhanced manifold pretrained on 20 million cells.

Chemical perturbation model generating post-treatment transcriptomes for compounds and cell lines never screened, from SMILES structure and dose.

Contrastive transcriptome-text model for free-text search, zero-shot cell annotation and natural-language chat over bulk and single-cell RNA-seq.

Sequence-to-function model predicting cell-type- and disease-specific gene expression from DNA, trained on pseudobulk profiles from 22 million cells.

Knowledge-informed cross-species foundation model pre-trained on 101 million human and mouse single-cell transcriptomes to decipher gene regulation.

Perturbation target identification for single-cell transcriptomics, reading intervened genes off the difference between two inferred causal graphs.

Single-cell foundation model built on MAMBA2 state-space blocks, reading all 60,530 genes of a transcriptome without variable-gene selection.

Spatial transcriptomics foundation model using cross-attention over niche ligand genes, pretrained on 4.1M deconvolved human Visium samples.

Graph attention foundation model for spatial transcriptomics that assigns spatial domains zero-shot across gene panels, tissues, and technologies.

Geometric deep learning model generating context-aware protein representations across 156 cell-type contexts from a multi-organ single-cell atlas.

Single-cell foundation model pre-trained on 50 million cells for gene network inference, denoising, and cell type prediction.

Framework turning single-cell expression profiles into ranked gene-name sequences, letting off-the-shelf language models generate and annotate cells.

Variational autoencoder pretrained on 74 million human single-cell transcriptomes from the CELLxGENE Census for batch correction and cell typing.