All Competitors

Hierarchical single-cell foundation model that turns scRNA-seq profiles into zero-shot donor-level embeddings for disease and biomarker prediction.

Generative framework that reconstructs missing spatial transcriptomics regions by jointly predicting cell locations, cell types, and gene expression.

Transformer foundation model for single-cell ATAC-seq that embeds both cells and cis-regulatory elements for annotation and batch correction.

Latent diffusion model for generating single-cell gene expression profiles, pairing a permutation-invariant autoencoder with a diffusion transformer.

Single-cell latent diffusion model fine-tuned on 14.5 million CD4+ T cells to simulate transcriptomic effects of single-gene perturbations.

Perturbation-trained single-cell foundation models (up to 3B parameters) that jointly model genes, cells, and compounds for precision oncology tasks.

Multimodal LLM that tokenizes single cells into discrete VQ-VAE codebook tokens, letting one model reason jointly over transcriptomes and text.

Single-cell foundation model learning technology-agnostic cell embeddings by contrasting cell views rather than reconstructing gene expression counts.

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

Tabular foundation model adapted for extreme feature counts, enabling in-context prediction on wide omics tables with tens of thousands of features.

Single-cell foundation model with a Hyena backbone that translates across omics layers, predicting protein abundance from transcriptomes zero-shot.

Diffusion model for multichannel fluorescent cell microscopy, generating morphologically plausible images aligned to OpenPhenom phenotypic embeddings.

Predicts single-cell scRNA-seq coverage and scATAC-seq insertion profiles from DNA sequence, adapting the Borzoi trunk with a cell-specific decoder.

Multimodal biomedical framework aligning frozen single-cell and protein model encoders to an LLM's embedding space for zero-shot reasoning.

Kidney-specialized single-cell foundation model trained across four mammalian species for zero-shot cell-type annotation and batch integration.

Single-cell multimodal LLM generating natural-language descriptions of cell type, tissue, disease, and pathway activity from scRNA-seq profiles.

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.

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.