All Competitors

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

Single-cell RNA integration model using adversarial batch training to embed and label cells from a new study without supplying a batch ID.

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.

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

Single-cell cytometry model that tokenizes each cell as marker-expression pairs, letting studies with different antibody panels share one encoder.

Single-cell metabolome inference from scRNA-seq, learned from spatially paired Visium and MALDI-MSI sections by multiple-instance learning.

Multimodal single-cell foundation model pretrained on 4M+ co-assayed cells, predicting 382 surface proteins from transcriptomes alone, zero-shot.

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

Visible neural network simulating eukaryotic cell growth by embedding the Gene Ontology into its architecture for interpretable phenotype prediction.

Single-cell multi-omics foundation model with a Mamba backbone, pretrained on 2.7 million paired scRNA-seq and scATAC-seq profiles.

Contrastive transcriptome-text model for free-text search, zero-shot cell annotation and natural-language chat over bulk and single-cell RNA-seq.

Single-cell foundation model that predicts latent representations of graph-connected gene blocks instead of reconstructing individual gene counts.

Cross-modal continued pretraining on curated mass-spectrometry proteomes lifts a 70M single-cell model past RNA-only checkpoints far larger.

Single-cell perturbation prediction by conditional flow matching, using one cell-type-conditioned model in place of a separate model per cell type.

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

Fine-grained cell-type abundance prediction from H&E histology, transferring to unseen cohorts and large slide archives without any retraining.

Multimodal transformer predicting alternative splicing outcomes across C. elegans neuron subtypes, reaching Spearman ρ = 0.88 on held-out exons.

Single-cell RNA-seq framework for hierarchical cell type annotation, unknown cell type detection, and surface protein imputation from transcriptomes.

Multi-task cellular foundation model predicting drug sensitivity, perturbation expression and drug-protein binding from one pretrained checkpoint.

Variational autoencoder trained on scRNA-seq and applied frozen to impute unmeasured genes and denoise spatial transcriptomics profiles.

Single-cell foundation model pretrained on 21 million mouse scRNA-seq profiles, with ortholog conversion extending its use to human transcriptomes.

Cell phenotyping model for spatial proteomics using a language-informed vision transformer to classify cell types zero-shot across marker panels.

Single-cell foundation model with a Hyena backbone that translates across omics layers, predicting protein abundance from transcriptomes zero-shot.