All Competitors
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Applications
Architectures
Learning Paradigms
Biological Subjects
Showing 1–10 of 10 filtered models
AlphaGenome
Google DeepMind
Google DeepMind model that predicts thousands of functional genomic tracks at single base-pair resolution from megabase-scale DNA sequences.
TranscriptFormer
Chan Zuckerberg Initiative
A generative cross-species foundation model for single-cell transcriptomics, trained on 112 million cells from 12 species spanning 1.5 billion years of evolution.
Borzoi
Calico Life Sciences
Deep learning model predicting cell-type-specific RNA-seq coverage at 32 bp resolution from 524 kb of DNA sequence, jointly modeling transcription, splicing, and polyadenylation.
GPTCelltype
Columbia University / Duke University
An R package that uses GPT-4 to annotate cell types in scRNA-seq data from marker genes, matching expert accuracy across hundreds of cell types and tissues.
scGPT
Bowang Lab
A generative pre-trained transformer for single-cell multi-omics, pretrained on 33 million human cells for cell annotation, batch correction, and perturbation prediction.
scDisInFact
Zhang Lab
Disentangled VAE framework for joint batch correction, condition-key-gene detection, and perturbation prediction in multi-batch multi-condition scRNA-seq data.
seq2cells
GSK.ai
Transfer learning framework that predicts single-cell gene expression from ~200kb DNA sequences using Enformer embeddings and a lightweight MLP.
Nucleotide Transformer
InstaDeep
A family of DNA foundation models (500M–2.5B parameters) trained on 3,200+ human genomes and 850 species for genomic sequence understanding and variant effect prediction.
Enformer
Google DeepMind
Transformer model that predicts gene expression and regulatory activity from 200kb DNA sequences, capturing enhancer-promoter interactions up to 100kb away.
scVAE
Technical University of Denmark / University of Copenhagen
Variational autoencoder for single-cell RNA-seq that models raw count distributions directly, producing latent cell representations without normalization preprocessing.