All Competitors

Multi-modal transformer fusing LLM gene embeddings with biological knowledge graphs to predict single-cell responses to genetic perturbations.

Single-cell foundation model producing species-agnostic cell embeddings by representing genes through frozen ESM-2 protein language model embeddings.

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

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

Bulk transcriptome foundation model, 150M parameters over ~20,000 protein-coding genes. Imputes masked expression at Pearson r = 0.954.

Single-cell RNA-seq foundation model pretrained only on malignant cells, for zero-shot batch integration and drug response prediction in tumors.

Knowledge-graph foundation model for drug repurposing, grounding a biomedical graph in cell-type-specific genetic associations to rank indications.

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

Reasoning LLM for single-cell type annotation, mapping per-cell expression to labels with marker-by-marker chains of thought on one GPU.

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

DNA methylation foundation model reconstructing genome-wide profiles from sparse input. Outperforms GrimAge2 aging clocks on mortality prediction.

Sequence-to-function model predicting cell-type- and disease-specific gene expression from DNA, trained on pseudobulk profiles from 22 million cells.

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

Pretrained transformer for cell type annotation of scRNA-seq data. Trained on 1.1M cells; outperforms supervised methods on cross-dataset transfer.

TCR-epitope binding prediction and tumor-reactive T-cell identification in one heterogeneous graph transformer, reaching AUROC 0.937 on IEDB.

Chromatin loop caller for Hi-C, Micro-C, DNA SPRITE, and single-cell contact maps, pairing axial attention with a U-Net to work at very low coverage.

Contrastive alignment framework that projects H&E histology and single-cell transcriptomic foundation model embeddings into one shared latent space.

Genomics foundation model that represents individual DNA fragments in a learned semantic space for cell-free DNA cancer detection and cell typing.

Histopathology model that predicts single-cell type composition and reconstructs spatial gene expression from H&E slides, with no molecular assay.

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

Spatial transcriptomics foundation model learning subcellular transcript positions and cell-niche context from 17 million Xenium single cells.

scVI variational autoencoder trained on the Tahoe-100M drug-perturbation atlas, giving a 10-dimensional embedding of treated cancer cell states.

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.