All Competitors

Every biological foundation model, evaluated and ranked by the bio.rodeo team

Showing 1–13 of 13 filtered models

  • Lingshu-Cell

    —1—
    DAMO AcademyMarch 26, 2026diffusionfoundation_modelgene_expression+4

    A generative cellular world model that uses masked discrete diffusion to learn whole-transcriptome scRNA-seq distributions and simulate perturbation responses across tissues and species.

    Single-cell
    21Openness

  • SCALE

    ———
    Shanghai AI LaboratoryMarch 17, 2026flow_matchingfoundation_modelgenerative+4

    Virtual cell foundation model pairing LLaMA-based cellular encoding with set-aware conditional flow matching to predict single-cell perturbation responses at atlas scale.

    Single-cell
    19Openness

  • AetherCell

    —2—
    Sun Yat-sen UniversityMarch 13, 2026drug_repurposingfoundation_modelgenerative+6

    Generative virtual-cell engine that unifies clinical RNA-seq and perturbation-assay data to predict transcriptomic responses to unseen compounds and genetic perturbations across biological scales.

    Single-cellSmall molecule
    17Openness

  • MAP

    ———
    Shanghai Jiao Tong UniversityFebruary 25, 2026contrastive_learningdrug_response_predictiongraph_neural_network+6

    A knowledge-driven framework that predicts single-cell transcriptomic responses to small molecules, including zero-shot prediction for drugs with no prior perturbation profiles.

    Single-cellSmall molecule
    12Openness

  • STACK

    1368—
    Arc Institute +1 otherJanuary 9, 2026cell_representationfoundation_modelin_context_learning+5

    Single-cell foundation model using tabular attention over context cells to enable zero-shot representation and in-context prediction of arbitrary perturbations.

    Single-cell
    33Openness

  • SCimilarity

    248112—
    GenentechNovember 20, 2024autoencodercell_type_annotationcontrastive_learning+4

    Metric learning foundation model that embeds single-cell RNA-seq profiles into a unified space for scalable cell type annotation and cross-atlas similarity search across tens of millions of cells.

    Single-cell
    78Openness

  • MuBind

    271—
    Theis LabAugust 8, 2024chromatincnndna+7

    Deep learning model predicting single-cell read counts from DNA sequence features and cell transition graphs to identify transcriptional regulators.

    Single-cell
    59Openness

  • GEARS

    369335—
    SNAP (Stanford)August 17, 2023crispr_screen_analysisgenetic_interaction_predictiongenomics+5

    Geometric deep learning model that predicts transcriptional responses to multi-gene perturbations by integrating single-cell RNA-seq with a gene-gene knowledge graph.

    Single-cell
    68Openness

  • scTranslator

    966—
    TencentAILabHealthcareJuly 4, 2023cross_modality_translationfoundation_modelgenerative+6

    Pre-trained large generative model that translates single-cell transcriptomes to proteomes, inferring missing single-cell protein abundance from RNA expression without alignment.

    Single-cell
    33Openness

  • tGPT

    17561.7K
    Tianjin Medical University Cancer Institute and HospitalApril 20, 2023cell_type_annotationdimensionality_reductionfoundation_model+5

    GPT-based generative model pre-trained on 22 million single-cell transcriptomes using rank-based gene encoding for single-cell clustering, trajectory inference, and bulk tumor analysis.

    Single-cell
    50Openness

  • CellOracle

    463562—
    Morris LabFebruary 8, 2023chromatingene_regulatory_network_inferencegraph_neural_network+6

    Machine learning framework for inferring cell-type-specific gene regulatory networks from single-cell multi-omics data and simulating transcription factor perturbations in silico.

    Single-cell
    18Openness

  • CPA

    146287—
    Theis LabApril 15, 2021autoencoderdrug_response_modelinggenerative+5

    Compositional Perturbation Autoencoder that predicts single-cell transcriptional responses to unseen drug combinations and doses using disentangled latent representations.

    Single-cell
    78Openness

  • scGen

    348617—
    Theis LabJuly 29, 2019batch_correctiongenerativeout_of_distribution_generalization+4

    Variational autoencoder that predicts single-cell perturbation responses across cell types and species using latent space vector arithmetic.

    Single-cell
    43Openness