All Competitors

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.

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.

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

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

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

Single-cell foundation model producing species-agnostic cell embeddings by representing genes through frozen ESM-2 protein language model embeddings.

Perturbation prediction model that forecasts transcriptional responses to multi-gene CRISPR perturbations from scRNA-seq and a gene-gene graph.

Explainable autoencoder for transcriptome analysis that uses SHAP attribution on latent variables to identify critical genes driving gene expression.

Generative transformer that translates single-cell transcriptomes into proteomes, inferring missing protein abundance from RNA expression alone.

Single-cell foundation model pretrained on about 30 million human transcriptomes, using rank-value encoding for context-aware gene network inference.

Single-cell foundation model pre-trained on 22 million transcriptomes, using rank-based gene encoding for clustering and trajectory inference.

Transformer framework for single-cell multi-omics that predicts cross-modality relationships using heterogeneous graphs of cells, genes, and proteins.

Pretrained transformer for cell type annotation of scRNA-seq data. Trained on 1.1M cells; outperforms supervised methods on cross-dataset transfer.

Single-cell perturbation prediction model that forecasts transcriptional responses to drug combinations and doses never experimentally measured.

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