All Competitors

Hi-C foundation model pretrained on 118 million contact submatrices, fine-tuned for loop detection, resolution enhancement and epigenomic prediction.

Resolution enhancement for sparse single-cell Hi-C contact matrices, using a cascading residual GAN with self-attention over chromatin loci.

Single-cell RNA-seq representation model that separates batch-dependent from batch-independent variation to compare disease states across datasets.

Spatial virtual-cell foundation model that simulates single-cell gene expression at chosen positions in real human tumor tissue.

Drug-conditional adapter inside a frozen single-cell foundation model, predicting transcriptional responses to unseen drugs and cell lines.

Receptor activity inference from bulk or single-cell transcriptomes, reading the genes a receptor regulates instead of the receptor's own expression.

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 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.