All Competitors

T-cell clonal expansion detection from scRNA-seq alone, without paired TCR sequencing. Trained on 2.6M pan-cancer T cells, reaching 0.85-0.96 AUROC.

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

Transcriptome-guided diffusion model generating Cell Painting images for unseen perturbations, improving MOA retrieval accuracy by 16.9% over IMPA.

Patient-level representation learning from scRNA-seq: a transformer set encoder with a diffusion decoder, fine-tuned for clinical prediction.

Generative model that restores cytoplasm-enriched genes lost in snRNA-seq, recovering cell-cell communication signals from raw nuclear counts.

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

Single-cell ATAC-seq foundation model pretrained on 2.8 million cells across 1.15 million chromatin regions via masked peak reconstruction.

Single-cell transcriptomics model fine-tuned on 1.12M CAR-T profiles to annotate T cell subtypes and predict therapy response and neurotoxicity.

Single-cell perturbation prediction by conditional flow matching, using one cell-type-conditioned model in place of a separate model per cell type.

Single-cell multi-omics foundation model whose three-stage pretraining and distillation yield RNA-and-ATAC-aware embeddings from RNA-only input.

Spatial transcriptomics foundation model pairing gene-scale cell embeddings with an SE(2) Transformer over cell coordinates, pretrained on 88M cells.

Cytometry foundation model for immunology, pretrained by masked-marker prediction on over 100 million immune cells from 53 public ImmPort studies.

Cross-species brain spatial transcriptomics foundation model pretrained on 133M cells from human, macaque, marmoset, and mouse whole brains.

Zebrafish single-cell foundation model built on the Geneformer framework, producing frozen gene and cell embeddings for developmental analysis.

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

Multimodal single-cell foundation model pretrained on 4M+ co-assayed cells, predicting 382 surface proteins from transcriptomes alone, zero-shot.

Spatial transcriptomics foundation model pretrained on 22 million cells, encoding each cell with its neighbors for niche and density prediction.

Single-cell perturbation foundation model predicting transcriptomic responses to CRISPR and small-molecule interventions in cancer cells.

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.

Hierarchical immune cell type annotation for scRNA-seq, rendering expression as images for a CNN. 93.2% mean subtype accuracy over seven datasets.

Multimodal transformer predicting alternative splicing outcomes across C. elegans neuron subtypes, reaching Spearman ρ = 0.88 on held-out exons.

Single-cell perturbation response prediction using dual conditional diffusion bridges that link unpaired control and perturbed populations.