All Competitors
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Applications
Architectures
Learning Paradigms
Biological Subjects
Showing 1–14 of 14 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.
Evo 2
Arc Institute
Genomic foundation model trained on 9.3 trillion DNA base pairs spanning all domains of life, with 40B parameters and a 1-million-token context window.
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.
SPIRED-Fitness
Tsinghua University
End-to-end framework predicting protein structure and mutational fitness from a single sequence, with 5x faster inference than ESMFold at comparable accuracy.
Caduceus
Kuleshov Lab
Bidirectional, reverse-complement equivariant DNA language models built on Mamba SSMs. Outperforms models 10x larger on long-range variant effect prediction.
GPN
Song Lab
A DNA language model for unsupervised genome-wide variant effect prediction, trained on multispecies genomes via masked language modeling without functional annotation labels.
GPN-MSA
UC Berkeley
Transformer-based DNA language model using whole-genome multispecies alignments for genome-wide variant effect prediction across coding and non-coding regions.
SaProt
Westlake University
Protein language model combining amino acid and Foldseek 3Di structural tokens, outperforming ESM-2 across 10 downstream tasks including mutation effect prediction.
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.
CARP
Microsoft Research
CNN-based protein language model series showing convolutions match transformer performance on sequence pretraining while scaling linearly with sequence length.
Enformer
Google DeepMind
Transformer model that predicts gene expression and regulatory activity from 200kb DNA sequences, capturing enhancer-promoter interactions up to 100kb away.
ESM-1v
Meta AI
Protein language model for zero-shot prediction of mutation effects, achieving state-of-the-art accuracy on deep mutational scanning benchmarks without MSA generation.
ESM-1b
Meta AI
Transformer protein language model trained on 250 million protein sequences that learns structural and functional representations without supervision.