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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1537–1560 of 2336 models
Spike inference from calcium imaging traces, driven by a multistate GCaMP kinetic model that generates the synthetic data its decoders are trained on.
Autoregressive graph transformer generating molecules as node and edge token sequences, fine-tunable for goal-directed design and property prediction.
Text-guided protein design framework aligning language with sequences for text-conditioned generation, zero-shot editing, and property prediction.
Cryo-ET particle picking model that localizes and classifies multiple protein complexes in a tomogram with a single 3D U-Net forward pass.
Variational autoencoder that learns interpretable representations of protein subtomograms from cryo-ET, trained on 5.8 million synthetic particles.
Cryo-ET segmentation framework adapting SAM2 to vesicles and membrane-bound compartments in tomograms and 2D micrographs, zero-shot or fine-tuned.
Phage protein function annotation from sequence, assigning hierarchical functional categories from frozen protein language model embeddings.
Microbiome community foundation model pretrained on 263,302 samples, encoding genus abundance as ranked tokens for classification and generation.
Transformer predicting microbial gene expression from an annotated genome alone, using protein language model embeddings of every coding sequence.
RNA foundation model unifying sequence representation, 3D structure prediction, and de novo design. Ranks first on 11 of 13 BEACON tasks.
Protein-text foundation model aligning sequences with function descriptions through segment-wise objectives for static and dynamic functional sites.
Bulk RNA-seq foundation model that learns patient-level embeddings from binned gene expression for pan-cancer classification and survival prediction.
DNA methylation foundation model that encodes 5mC as a fifth base, pretrained on 568 million BS-seq reads for tissue-of-origin and expression tasks.
Instance segmentation for nervous-system tissue, trained only on biophysical simulations and applied to real brain, spinal cord and nerve sections.
Multi-omics instruction-tuned LLM that reads DNA, RNA, protein, and multi-molecule sequences and answers natural-language questions about them.
Base-resolution chromatin accessibility model that factors out enzyme sequence bias to score regulatory variants and transcription factor footprints.
Phosphorylation site prediction from protein sequence. A LoRA-adapted ESM-2 encoder feeds a conformer, reaching 79.5% AUC at serine sites.
Chromatin loop caller for Hi-C, Micro-C, DNA SPRITE, and single-cell contact maps, pairing axial attention with a U-Net to work at very low coverage.
Cell Painting generative model encoding lab, batch, and well position as causal variables, predicting mechanism and target for unseen compounds.
Graph neural network counting recurring cell-type neighborhood motifs in spatial transcriptomics and proteomics, linking topology to phenotype.
Multiomic foundation model for zero-shot in silico perturbation, predicting gene regulation and cell fate transitions from DNA and ATAC signal.
Tilt interpolation for cryo-electron tomography, synthesizing intermediate projections to improve angular sampling without extra electron dose.
Graph neural network that predicts magnesium ion binding sites on RNA structures, powering SAXS-based validation of RNA solution conformations.