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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 385–400 of 400 filtered models
BERT-based predictor of DNA N6-methyladenine (6mA) modification sites, using word2vec encoding and cross-species transfer learning.
Transformer that imputes missing CpG methylation states from sparse single-cell bisulfite sequencing, modeling genomic and cell-level structure.
Multi-modal self-supervised transformer for regulatory genomics, pre-trained on DNA sequence together with transcription factor binding matrices.
Transformer that predicts gene expression and epigenomic signals from 200kb of DNA sequence, capturing distal enhancers up to 100kb from a promoter.
Drug repurposing model that predicts a compound's L1000 transcriptional signature from SMILES and ranks it against a disease gene signature.
Bidirectional transformer for DNA using k-mer tokenization, fine-tunable for promoter, splice site, and transcription factor binding prediction.
Chromatin interaction prediction from DNA sequence alone, calling CTCF-, RNA Pol II- and Hi-C-associated loops between open chromatin regions.
Cross-species convolutional network trained jointly on human and mouse genomes to predict regulatory sequence activity and noncoding variant effects.
Sparse attention transformer that extends BERT to 8x longer sequences via random, local, and global attention, with genomic sequence applications.
RNA-binding protein target site prediction from 1,000 bp of sequence, scoring how a noncoding variant disrupts binding across 88 RBPs.
Chromatin feature prediction from 2 kb of DNA, scoring 2,002 transcription factor, DNase and histone profiles to rank noncoding variant effects.
Tissue-specific gene expression prediction from DNA sequence, scoring a noncoding variant as the log fold change it causes in each of 218 tissues.
Dilated convolutional network that predicts cell-type-specific epigenetic and transcriptional profiles from DNA sequence across mammalian genomes.
Attention-based model predicting gene expression from histone modification signals across 56 cell types, with interpretable attention scores.
Convolutional neural network that predicts DNA accessibility from sequence across 164 DNase-seq cell types, enabling variant effect prediction.
Noncoding variant effect prediction from DNA sequence, scoring how an allele shifts 919 chromatin features across ENCODE and Roadmap cell types.