All Competitors

Single-cell foundation model trained by metric learning to embed scRNA-seq profiles for cell type annotation and similarity search in cell atlases.

Single-cell perturbation prediction model that adds gene-level language embeddings from NCBI, UniProt, and Gene Ontology to scGPT representations.

Multi-modal, multi-task biological foundation model trained on 2 billion samples spanning proteins, small molecules, and single-cell gene expression.

Knowledge-informed cross-species foundation model pre-trained on 101 million human and mouse single-cell transcriptomes to decipher gene regulation.

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.

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.

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.