All Competitors

Patient-level single-cell foundation model that condenses a donor's scRNA-seq profile into one 288-dimensional embedding for disease cohort search.

DNA methylation foundation model over 49,156 array CpG sites. Imputes missing values, embeds samples, and predicts epigenetic age and disease risk.

Single-cell foundation model that maps new scRNA-seq datasets onto a metacell coordinate system zero-shot, without batch correction or fine-tuning.

Large causal cell model trained on cancer perturbation data that generalizes zero-shot to patient-derived cells for therapeutic target prioritization.

Spatially aware transcriptomic foundation models for cancer, pairing 50um-Local and 250um-Extended views of spot-resolution spatial transcriptomes.

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

Spatial transcriptomics resolution enhancement from expression alone, using a tri-oriented Mamba encoder to predict expression between capture spots.

Self-supervised contrastive model embedding cell and organelle dynamics from time-lapse microscopy for cell-state analysis without manual labels.

Red blood cell morphology foundation model pretrained on 1.25 million single-cell crops, released as small, base, and large ViT feature extractors.

T-cell receptor specificity prediction that separates general antigens from autoimmune-related ones using ESM-2 embeddings and a topology-aware graph.

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.

Meta-learning framework that transfers perturbation responses across cell lines, donors, and drugs from a few measured seed perturbations.

Context-specific protein embeddings across 286 liver disease and cell-type combinations, learned over interactomes built from a single-cell atlas.

Contrastive multimodal model for perturbation screens, aligning transcriptomic signatures with text and cell-painting image embeddings.

Pseudo-membrane generator for fluorescence microscopy that synthesizes missing membrane staining from nuclei to improve single-cell segmentation.

Single-cell perturbation prediction model trained only on synthetic priors, inferring drug targets, intervention strengths, and regulatory graphs.

Spatial transcriptomics foundation model using cross-attention over niche ligand genes, pretrained on 4.1M deconvolved human Visium samples.

Vision transformers trained on Human Protein Atlas fluorescence microscopy for subcellular protein localization and cell morphology representation.

Single-cell epigenomic foundation model that reads scATAC-seq as cell sentences of accessible cCREs, pretrained on about 5 million human cells.

Generative transformer that writes candidate cognate epitope sequences from a TCR CDR3-beta input, annotating repertoires without functional assays.

Vision-guided model that builds virtual 3D organoid surrogates from brightfield microscopy to predict chemical perturbation responses without omics.

Histopathology model reconstructing tissue-wide single-cell gene expression from H&E slides, using sparse TMA measurements as molecular anchors.

Deep learning framework that predicts DNA methylation from genomic sequence across 39 human tissues, with an scRNA-seq variant for unseen cell types.