All Competitors
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Applications
Architectures
Learning Paradigms
Biological Subjects
Showing 1–8 of 8 filtered models
SFM-Protein
Microsoft Research
A transformer protein language model using integrative co-evolutionary pre-training to capture both short-range and long-range residue interactions from sequence alone.
DNABERT-S
MAGICS Lab
Species-aware DNA embedding model built on DNABERT-2, using contrastive learning to cluster and differentiate genomic sequences by species without labeled data.
Ankh
Technical University of Munich
Optimized protein language model that surpasses state-of-the-art performance with fewer than 10% of the parameters of comparable models.
CARP
Microsoft Research
CNN-based protein language model series showing convolutions match transformer performance on sequence pretraining while scaling linearly with sequence length.
ProtTrans
Rostlab
A suite of six protein language models — including ProtBERT and ProtT5 — trained on up to 393 billion amino acids using large-scale HPC infrastructure.
ESM-1b
Meta AI
Transformer protein language model trained on 250 million protein sequences that learns structural and functional representations without supervision.
TAPE
UC Berkeley
Benchmark suite of five biologically relevant tasks for evaluating protein sequence representation learning, covering structure, homology, and engineering.
UniRep
Church Lab
A multiplicative LSTM protein language model trained on 24M sequences to produce fixed-length embeddings for protein engineering and function prediction.