All Competitors

Single-cell RNA-seq foundation model pretrained on 50 million human cells, encoding the full transcriptome for annotation and perturbation modeling.

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

Single-cell foundation model fusing scRNA-seq with protein structure embeddings and subcellular localization priors through cross-attention.

Multi-view single-cell foundation model at 12B parameters, aligning expression and perception views with an LLM-JEPA joint-embedding objective.

Single-cell foundation model with 800M parameters trained on ~100 million human cells, for annotation, perturbation prediction, and gene analysis.

Transfer learning framework that predicts single-cell gene expression from ~200kb DNA sequences using Enformer embeddings and a lightweight MLP.

Conversational T5-based framework that turns scRNA-seq data into cell sentences for cell type annotation and drug sensitivity prediction.

Generative language model for single-cell transcriptomics with 368M parameters, unifying cell type annotation, batch integration, and cell generation.

Single-cell foundation model trained on 30 million transcriptomes, binning genes by expression rank to give context-aware gene and cell embeddings.

Single-cell foundation model with rank and expression-aware input streams, pairing masked gene modeling with cell-level contrastive learning.

Single-cell foundation model adding a gated cell-level contrastive objective to masked expression pretraining for transferable frozen cell embeddings.

World model that simulates a human cell as one persistent state, propagating drug and gene perturbations from DNA through to whole-cell morphology.

Single-cell foundation model that treats cells as tokens and tissues as sentences, encoding cell-cell relationships across 85M parameters.

860M-parameter generative single-cell foundation model that jointly represents and generates epigenomic, transcriptomic, and proteomic modalities.

Single-cell language model that prepends biomedical knowledge-graph tokens to cell sentences, grounding cell type annotation in pathway structure.

Transcriptomic foundation model pretrained on 67M single-cell and spatial profiles, modeling gene expression and inter-cellular dependencies.

Single-cell foundation model for yeast that injects regulatory network priors into transformer attention for zero-shot and fine-tuned analysis.

Single-cell foundation model built on masked discrete diffusion, jointly generating gene identities and expression values from 59 million cells.

Virtual-cell model that compresses a transcriptome into eight discrete tokens in a reasoning LLM's vocabulary, predicting module-level drug response.

Single-cell foundation model domain-adapting Llama-3.1-8B on 1.3M gastric cancer cells with gene-family cell sentences instead of ranked-gene order.

Single-cell foundation model tokenizing expression profiles with a residual-VQ autoencoder to generate new cells of a type from held-out examples.

Single-cell foundation model that learns cell and gene embeddings by latent-space self-distillation rather than reconstructing masked expression.

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