All Competitors

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

Unsupervised VAE-GAN hybrid profiling single-cell morphology in a 10-dimensional disentangled latent space transferable across imaging modalities.

Single-cell foundation model trained on 30 million transcriptomes, binning genes by expression rank to give context-aware gene and cell embeddings.

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

Single-cell Hi-C model annotating chromatin loops, TAD-like domains, and A/B compartments from sparse contact maps with no dense imputation.

Graph attention foundation model for spatial transcriptomics that assigns spatial domains zero-shot across gene panels, tissues, and technologies.

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

Geometric deep learning model generating context-aware protein representations across 156 cell-type contexts from a multi-organ single-cell atlas.

Single-cell foundation model pre-trained on 50 million cells for gene network inference, denoising, and cell type prediction.

Framework turning single-cell expression profiles into ranked gene-name sequences, letting off-the-shelf language models generate and annotate cells.

Variational autoencoder pretrained on 74 million human single-cell transcriptomes from the CELLxGENE Census for batch correction and cell typing.

Single-cell foundation model with 800M parameters trained on ~100 million human cells, for annotation, perturbation prediction, and gene analysis.

Single-cell transcriptomics foundation model with 100 million parameters, pretrained on over 50 million human scRNA-seq profiles for cell embeddings.

Single-cell foundation model that treats cells as tokens and tissues as sentences, encoding cell-cell relationships across 85M parameters.

Transformer foundation model pretrained on 110M single-cell and spatial transcriptomics profiles, transferring spatial context to dissociated cells.

Generative pretrained transformer trained on 33 million human cells for single-cell annotation, batch correction, and perturbation prediction.

Conversational T5-based framework that turns scRNA-seq data into cell sentences for cell type annotation and drug sensitivity prediction.

Causal discovery foundation model that aggregates classical FCI or GIES estimates over small variable subsets into a global gene regulatory graph.

Generative language model for single-cell transcriptomics with 368M parameters, unifying cell type annotation, batch integration, and cell generation.

Generative image perturbation autoencoder predicting cellular morphological responses to chemical and genetic perturbations from control images.

Deep graph contrastive learning framework for single-cell proteomics embedding, handling peptide uncertainty, missingness, and batch effects.

Diffusion model for synthesizing single-cell RNA-seq data, with guided generation of specific cell types, rare cells, and developmental trajectories.

Cell type annotation for single-cell RNA-seq that builds a graph per signaling pathway, learning across pathway views with graph neural networks.