All Competitors

Generative single-cell foundation model trained on 112 million cells from 12 species, autoregressively modeling gene identities and expression counts.

Single-cell foundation model inferring context-specific protein-protein interactions from cancer transcriptomes via a variational graph autoencoder.

Single-cell type annotation pairing a CellMarker-derived knowledge graph with multi-agent LLM retrieval, generalizing across 11 tissue types.

Single-cell foundation model built on bidirectional Mamba blocks and pretrained on 30 million cells for linear-time transcriptome embedding.

Single-cell foundation model contrastively fine-tuned on genome-scale Perturb-seq data to separate perturbed from unperturbed transcriptomic states.

Multi-LLM consensus framework for automated cell type annotation in scRNA-seq data, outperforming prior methods by ~15% in mean accuracy.

Multi-modal single-cell foundation model that projects Enformer DNA embeddings into a transcriptome model token space to predict gene regulation.

Single-cell foundation model pre-trained on 50 million cells that infers cell-specific gene regulatory networks from transformer attention matrices.

Single-cell RNA-seq encoder trained with contrastive learning to merge plate- and droplet-based protocols, zero-shot on unseen tissues.

Spatial transcriptomics foundation model pretrained to generate a cell's expression profile from its neighbors, yielding zero-shot niche embeddings.

Single-cell foundation model reading scRNA-seq profiles as ranked gene-name sentences, scaled on Gemma-2 for annotation, reasoning and drug screens.

Single-cell multi-omics foundation model with a Mamba backbone, pretrained on 2.7 million paired scRNA-seq and scATAC-seq profiles.

Enhancer models predicting cell-type-specific chromatin accessibility from DNA sequence, with a pretrained zoo and synthetic enhancer design tools.

Multimodal graph foundation model fusing single-cell expression, biomedical text, and signaling networks, pretrained on ~80M sc/snRNA-seq profiles.

Genomic language model for scRNA-seq cell-type annotation, reweighting rare classes so diseased cell types are not swamped by common ones.

Single-cell RNA integration model using adversarial batch training to embed and label cells from a new study without supplying a batch ID.

Single-cell model inferring which developmental signaling pathways are active from scRNA-seq, trained on combinatorial stem-cell perturbation screens.

Single-cell foundation model that fuses scRNA-seq profiles with text, pairing a cell encoder with an LLM for cell annotation and clustering.

Single-cell RNA-seq foundation models combining masked modeling with ontology supervision to classify cell states across unseen donors and diseases.

Structure-aware adapter that injects chromatin-derived gene regulatory networks into single-cell RNA foundation models like scGPT and scFoundation.

Latent diffusion model for single-cell multi-omics generation and modality translation, with gradient-based inference of gene regulatory networks.

Inverse molecular design conditioned on transcriptomics, generating small molecules intended to revert a diseased cell to a healthy expression state.

Single-cell DNA methylation foundation model capturing genome-wide CpG dependencies in whole-genome bisulfite sequencing across tissues and species.