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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 145–168 of 201 filtered models
RNA foundation models that learn their own character-level tokenization instead of fixed nucleotide or k-mer vocabularies. Sizes run 8M to 650M.
RNA language model built from bidirectional Mamba2 blocks with a flash-attention head, pretrained on 100 million sequences up to 2,048 nucleotides.
Whole-genome somatic copy-number aberration prediction from bulk RNA-seq alone, with one pan-cancer model covering 33 tumor types.
Poly(A)-tail length change predicted from mRNA 3' UTR sequence in maturing oocytes, scoring how single-nucleotide variants disrupt tail lengthening.
Demultiplexer for direct RNA nanopore sequencing that basecalls the DNA barcode inside the RT adapter, reaching 99% precision on up to 96 barcodes.
Dual-language transformer pretrained on paired protein and mRNA coding sequences, scoring protein and mRNA properties and generating optimized CDS.
Genomic language model that labels adapter sequences in nanopore direct-RNA reads base by base, then splits the chimeric reads those adapters create.
All-atom structure tokenizer that turns proteins, RNA and small molecules into discrete 3D tokens and decodes them back below 1 Å RMSE.
Direct RNA nanopore basecaller pairing a densely connected 1D CNN and CTC decoder with a Random Forest classifier that demultiplexes 24 barcodes.
Codon-tokenized mRNA language model whose hierarchical loss scores codon errors by synonymity, pretrained on 15.3M curated antibody mRNAs.
RNA folding kinetics model that predicts the full distribution of first passage times from a few simulated examples in a single forward pass.
Mixed-modal DNA, RNA, and protein foundation model at 110M and 270M parameters, with in-context learning across sequence modalities.
RNA foundation language model pretrained on mammalian and viral genomes, fine-tuned to predict translation efficiency, half-life, and splice sites.
Codon-resolution language model suite pairing a bidirectional encoder with an autoregressive decoder over protein-coding sequences.
Mamba-based mature RNA foundation model, contrastively trained on splice isoforms and 400+ mammalian species orthologs for mRNA property prediction.
RNA language model that predicts G-quadruplex formation and subtype from transcript sequence and scores how single-nucleotide variants alter folding.
Transformer-based generative language model for de novo RNA design, pretrained on 16 million non-coding RNA sequences from RNAcentral.
RNA language model adapted from ESM-2 by cross-modality transfer learning, matching RNA-native baselines with 1/8 the trainable parameters.
Conditional generator of bulk transcriptome and DNA methylation profiles, sampling tissue-, age- and species-matched synthetic omics samples.
Histopathology image translation with diffusion, moving H&E tiles between stains, tumor types, and organ sites and editing them from omics profiles.
RNA language model that switches between nucleotide and byte-pair tokenization by input length, so one 117M encoder handles sequences of any length.
Cell-free RNA language model for multi-cancer detection, classifying plasma samples straight from raw sequencing reads without gene annotation.