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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 673–696 of 2336 models
Photoplethysmography foundation model whose hierarchical convolutional encoder exposes one embedding per temporal scale and runs entirely on-watch.
Latent diffusion model that backmaps coarse-grained protein structures to all-atom detail, with bond geometry learned into the latent space.
Open-source Apache-2.0 reproduction of AlphaFold3 that predicts all-atom structures of proteins, RNA, DNA, small molecules, and their complexes.
Protein-ligand cofolding model that predicts 3D complex structures with SO(3)-equivariant diffusion, trained on physics-based synthetic data.
Synthetic 3D MRI generation spanning brain, prostate, breast and abdomen, with the T1, T2 or FLAIR contrast selected at sampling time.
Siamese protein language model whose embedding distances approximate TM-score and lDDT, enabling alignment-free protein structure comparison.
EEG foundation model whose learned queries map any electrode montage into a fixed latent space, scaling linearly in the number of channels.
Multimodal EHR foundation model that fuses polygenic risk scores into a GPT-2-style backbone by cross-attention for zero-shot disease risk prediction.
Vision Transformer foundation model for spatial metabolomics, pretrained on ~4,000 curated METASPACE mass spectrometry imaging datasets.
Perturbation-trained single-cell foundation models (up to 3B parameters) that jointly model genes, cells, and compounds for precision oncology tasks.
Multimodal molecular large language model grounding molecule understanding and generation in fine-grained, multi-level chemical knowledge.
Multimodal LLM that tokenizes single cells into discrete VQ-VAE codebook tokens, letting one model reason jointly over transcriptomes and text.
Sequence-based binding site predictor spanning protein-DNA, protein-RNA, protein-protein, and antibody-antigen interfaces via a fine-tuned ProtT5.
Structure prediction backbone that swaps AlphaFold3-style triangle attention for triangle multiplication, cutting compute without losing accuracy.
Self-supervised foundation model for human cortical cytoarchitecture, encoding histological brain sections into anatomically meaningful features.
fMRI foundation model of the human brain connectome: 1.2B parameters and brain-environment interaction tokens for behavior and disease prediction.
Transcription factor binding site prediction model that refines a DNABERT-2 backbone with contrastive learning across diverse TFBS types.
Vision-language encoders for chest CT that align 3D volumes with radiology reports using contrastive, report-generation, and masked-image objectives.
Self-supervised models that embed gut metagenomic abundance profiles for robust phenotype prediction in data-limited, cross-cohort settings.
Protein-protein docking scorer that ranks interface poses from image-encoded patches, swapping PIsToN's Vision Transformer for Vision Mamba.
All-atom generative model for de novo protein design using SE(3) flow matching over oriented residue rigid bodies.
Flow-matching model that predicts protein conformational ensembles across the order-disorder continuum, from folded domains to disordered chains.
EEG foundation model for brain-computer interfaces, pairing masked pretraining with a mixture-of-experts transformer across electrode montages.
Suite of large language models fine-tuned with LoRA to generate antigen-targeted antibody Fv sequences from an antigen and its epitope.