All Competitors

Channel-adaptive autoregressive generative model that synthesizes in-silico fluorescence microscopy of protein subcellular localization from amino-acid sequence and cellular landmark stains.

A DINO-pretrained vision transformer for accurate, robust segmentation of micronuclei in DNA-stained fluorescence microscopy across cell lines and instruments.

Generative single-cell foundation model trained on 100M+ transcriptomes that predicts how genetic perturbations reshape cellular trajectories over time.

A temporal diffusion-forcing generative framework for simulating, interpolating, and forecasting single-cell developmental dynamics from irregularly sampled time-series data.

A multimodal single-cell foundation model with a multiway Transformer that jointly models scRNA-seq and scATAC-seq, including RNA-only, ATAC-only, and paired inputs.

A hierarchical sequence-based protein representation that encodes proteins as discrete 'words' for zero-shot functional discovery and generative design.

Bidirectional generative framework linking single-cell nuclear morphology and gene expression, built on a morphology foundation model trained on 21M+ segmented nuclei.

Cross-modal cell-language foundation model that aligns single-cell expression with LLM-enriched textual descriptions using a noise-robust contrastive objective.

A multimodal architecture that couples pretrained DNA, RNA, and protein language models via directional cross-attention following the central dogma to form a unified Virtual Cell Embedding.

A native 3D vision transformer self-supervised on unlabeled fluorescence microscopy volumes that generalizes to unseen object classes without retraining or voxel annotations.

A conversational single-cell and spatial multi-omics brain agent pretrained on 130 million cells across species for zero-shot cell annotation and disease prediction.

Flow-matching generative model that simulates high-resolution fluorescence images of human fibroblasts conditioned on surface micro-topographies, a digital twin of cell-material interactions.

A scalable latent diffusion model for generating realistic single-cell gene expression profiles, using a permutation-invariant VAE and flow-matching diffusion transformer.

A fine-tuned scLDM variant trained on 14.5 million CD4+ T cells for counterfactual prediction of single-gene perturbation effects in immune cells.

A reasoning language model post-trained on virtual cell simulations to answer complex biological questions about gene perturbations in natural language.

Generalist cell segmentation model combining SAM's ViT-L backbone with Cellpose flow fields. First model to surpass average human annotators on the Cellpose benchmark.

Generalist cell segmentation framework with a super-generalist cyto3 model and one-click image restoration networks optimized for downstream segmentation quality.

Self-supervised Vision Transformer models trained on proteome-wide fluorescence microscopy images from the Human Protein Atlas for subcellular protein localization.

Single-cell RNA-seq foundation model series pretrained on 50 million human cells with full transcriptome-scale context for cell clustering and perturbation modeling.

A unified diffusion model enabling bidirectional transformation between protein amino acid sequences and fluorescence microscopy images for subcellular localization prediction.

A self-supervised contrastive learning method for embedding cell and organelle dynamics from time-lapse microscopy using temporal regularization and single-cell tracking.

An 800M-parameter single-cell foundation model pre-trained on 100 million human cells via a RetNet architecture for cell annotation, perturbation prediction, and gene analysis.

A suite of virtual staining models that translate label-free microscopy images into fluorescent-equivalent staining of nuclei and plasma membranes.

Single-cell transformer that treats cells as tokens and tissues as sentences, encoding cell-cell relationships with 100x faster inference than prior pre-trained models.