All Competitors
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1–12 of 12 filtered models
Cellpin
———A VAE trained on scRNA-seq reference data and applied frozen at inference to impute unmeasured genes and denoise spatial transcriptomics profiles.
Spatial omicsSingle-cell22OpennessChreode
———University of North Carolina at Chapel Hill +2 othersMay 27, 2026cell_fate_predictioncrispr_perturbationdevelopmental_trajectory_modeling+8A cell world model pretrained on a 2.4M-cell mouse embryonic atlas that predicts one-step transcriptional state transitions and transfers to perturbation prediction.
Single-cell26OpennessRegVelo
1538—Bayesian deep generative model that integrates gene regulatory networks into RNA velocity inference, enabling cell fate mapping and in silico perturbation of transcription factors.
Single-cell59OpennessAnewOmni
721—All-atom generative foundation model trained on 5M+ biomolecular complexes that designs small molecules, peptides, and nanobodies against a target site from one checkpoint.
ProteinSmall molecule63OpennessPLUM
1——A conditional variational autoencoder for controlled antimicrobial peptide design that disentangles sequence, function, and length in its latent space.
Protein56OpennessCryoLens
16——A variational autoencoder for interpretable 3D reconstruction and representation learning of protein subtomograms from cryo-ET data, trained on 5.8 million synthetic particles.
Imaging74OpennessscVI (CELLxGENE Census)
1.6K2.3K—A variational autoencoder pretrained on 74 million human single-cell transcriptomes from the CELLxGENE Census for scalable batch correction, cell typing, and data integration.
Single-cell96OpennessscDisInFact
1325—Disentangled VAE framework for joint batch correction, condition-key-gene detection, and perturbation prediction in multi-batch multi-condition scRNA-seq data.
Single-cell79OpennessRfamGen
4256—A VAE-based generative model that designs novel functional RNA sequences by encoding MSA and consensus secondary structure constraints from Rfam families.
RNA10OpennessDPI
356—End-to-end single-cell multimodal analysis framework using deep parametric inference to integrate RNA and protein data into a unified latent space.
Single-cell45OpennessscVAE
89——Technical University of Denmark +1 otherAugust 15, 2020autoencodergene_expressionvariational_autoencoderVariational autoencoder for single-cell RNA-seq that models raw count distributions directly, producing latent cell representations without normalization preprocessing.
Single-cell53Openness