All Competitors

Hierarchy-aware self-supervised model for single-cell microscopy that preserves morphological structure suppressed by imaging-modality confounders.

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

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

Self-supervised 3D masked autoencoder for volumetric fluorescence microscopy, aligned to ESM2 embeddings to predict protein localization.

Single-cell language model that prepends biomedical knowledge-graph tokens to cell sentences, grounding cell type annotation in pathway structure.

Generative microscopy foundation model that synthesizes in-silico fluorescence images of protein subcellular localization from amino-acid sequence.

Vision transformer trained with DINO self-supervision to segment micronuclei in DNA-stained fluorescence images across cell lines and microscopes.

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

Temporal diffusion framework for single-cell developmental dynamics, interpolating and forecasting cell states from irregularly sampled time series.

Multimodal single-cell foundation model whose multiway Transformer jointly models scRNA-seq and scATAC-seq from RNA-only, ATAC-only, or paired inputs.

Protein language model that encodes sequences as discrete words from a learned vocabulary for zero-shot function inference and protein design.

Bidirectional generative framework linking nuclear morphology and gene expression, built on a morphology model trained on 21 million segmented nuclei.

Cross-modal single-cell foundation model that aligns gene-expression profiles with LLM-enriched cell descriptions in a shared embedding space.

Multimodal architecture coupling pretrained DNA, RNA, and protein language models with directional cross-attention into one Virtual Cell Embedding.

Native 3D vision transformer self-supervised on unlabeled fluorescence microscopy volumes, segmenting subcellular structures without voxel labels.

Conversational single-cell and spatial multi-omics brain foundation model, with zero-shot cell annotation and disease prediction across species.

Flow-matching generative model that synthesizes fluorescence images of human fibroblasts conditioned on surface micro-topographies.

Generative framework that reconstructs missing spatial transcriptomics regions by jointly predicting cell locations, cell types, and gene expression.

Latent diffusion model for generating single-cell gene expression profiles, pairing a permutation-invariant autoencoder with a diffusion transformer.

Single-cell latent diffusion model fine-tuned on 14.5 million CD4+ T cells to simulate transcriptomic effects of single-gene perturbations.

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

Reasoning language model post-trained on virtual cell simulations, answering questions about gene perturbations and their effects in natural language.

Generalist cell segmentation model pairing SAM's ViT-L encoder with Cellpose flow fields, outperforming average human annotators on its benchmark.

Generalist cell segmentation pairing the cyto3 super-generalist model with one-click networks that denoise, deblur, and upsample microscopy images.