All Competitors
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1–18 of 18 filtered models
RhoFold+
ml4bio
End-to-end RNA 3D structure prediction combining the RNA-FM language model with Invariant Point Attention, achieving SOTA on RNA-Puzzles and CASP15.
Orthrus
Bowang Lab
Mamba-based mature RNA foundation model using contrastive learning on splice isoforms and 400+ mammalian species orthologs for mRNA property prediction.
GenerRNA
Preferred Networks
Transformer-based generative language model for de novo RNA sequence design, pre-trained on 16 million sequences to generate novel, structurally stable RNAs.
5' UTR-LM
Princeton University
A transformer language model pretrained on 5' UTR sequences across five species to predict mRNA translation efficiency, ribosome loading, and expression levels.
ERNIE-RNA
Tsinghua University
A structure-enhanced RNA language model that incorporates base-pairing constraints into self-attention, achieving state-of-the-art RNA structure and function prediction.
RiNALMo
LBCB Sci
650M-parameter RNA language model pre-trained on 36M non-coding RNA sequences. Achieves state-of-the-art generalization on secondary structure prediction across unseen RNA families.
RNAformer
University of Freiburg
Axial-attention transformer for RNA secondary structure prediction from single sequences, without MSAs. Achieves state-of-the-art accuracy via homology-aware training.
RNA-MSM
Peking University / Griffith University
Unsupervised RNA language model using multiple sequence alignments to predict secondary structure and solvent accessibility from evolutionary information.
RfamGen
Kyoto University / Waseda University
A VAE-based generative model that designs novel functional RNA sequences by encoding MSA and consensus secondary structure constraints from Rfam families.
ATOM-1
Atomic AI
RNA foundation model trained on chemical mapping data, achieving state-of-the-art accuracy in predicting RNA secondary structure, tertiary structure, and mRNA stability.
xTrimoGene
BioMap
Asymmetric encoder-decoder transformer for single-cell RNA-seq data that reduces FLOPs by 1-2 orders of magnitude while achieving state-of-the-art performance.
trRosettaRNA
Yang Lab
Deep learning pipeline for RNA 3D structure prediction using a transformer (RNAformer) to predict inter-nucleotide geometries, refined by Rosetta energy minimization.
UNI-RNA
DP Technology
BERT-based RNA foundation model pre-trained on 1 billion sequences, achieving state-of-the-art performance in secondary structure, tertiary structure, and functional annotation tasks.
MRM-BERT
Nanjing University of Science and Technology
A hybrid deep learning model predicting 12 types of RNA modifications by fine-tuning DNABERT representations fused with CNN-encoded sequence features.
SpliceBERT
Biomed AI
A BERT-based RNA language model pre-trained on 2M+ pre-mRNA sequences from 72 vertebrate species for splicing prediction and variant effect analysis.
RNA-FM
AI for Science (PKU)
A BERT-based RNA foundation model trained on 23.7 million non-coding RNA sequences, producing embeddings for structure prediction, functional annotation, and RNA design.
EMDLP
China University of Mining and Technology
Ensemble multiscale deep learning model for RNA methylation site prediction, combining dilated convolution and BiLSTM with multiple sequence encodings.
RNABERT
Keio University
A BERT-based model for RNA base embeddings that captures sequence context and secondary structure, enabling fast structural alignment and clustering.