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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1585–1608 of 2565 models
Sparse all-atom denoising models for de novo protein backbone generation, producing designable structures up to 1,000 residues in seconds.
Autoregressive genomic foundation models from 20M to 1B parameters that solve ten DNA tasks at once and map sequences to text and images.
Autoregressive graph generator that flattens molecules into token sequences, letting a decoder-only transformer sample valid structures in one pass.
Self-supervised 3D vision foundation model for non-contrast head CT, pretrained on 361,663 scans to detect a broad range of intracranial disease.
Multimodal ECG language model pairing a specialized signal encoder with a biomedical LLM for cardiovascular disease detection and question answering.
Attention architecture fusing docking scores with protein language model embeddings, so an enzyme gets a different representation per substrate.
Plant genomic foundation model with a 64 kb single-nucleotide context that predicts gene structures and generates de novo plant gene sequences.
Photoplethysmography foundation model pretrained on raw wearable signals from a field study, transferring across lab and field health tasks.
EEG foundation model for Alzheimer's disease detection, pretrained by contrastive learning across 13 clinical EEG datasets and 2,238 subjects.
Cryo-ET particle picking model that localizes six protein complexes in tomograms using an ensemble of lightweight 3D U-Nets.
Segment Anything fine-tuned for nucleus segmentation in histopathology, supporting automatic and interactive annotation on unseen tissue images.
Generalist cell segmentation pairing the cyto3 super-generalist model with one-click networks that denoise, deblur, and upsample microscopy images.
Prostate cancer detection model for MRI and transrectal ultrasound, trained with patch-level contrastive learning across 4,401 patients.
Cryo-ET particle picking model that averages tiny, medium, and large 3D U-Nets pretrained on simulated tomograms and fine-tuned on experimental data.
Cryo-ET particle picking model, an ensemble of 3D U-Nets with EfficientNet encoders that finds protein complexes in tomograms by heatmap segmentation.
Crossmodal diffusion model synthesizing bulk tumor gene expression from H&E whole-slide images, so grading and survival prediction need no RNA assay.
Cryo-ET particle picking ensemble of three 3D segmentation models predicting particle-center heatmaps with ResNet50d and EfficientNetV2-M backbones.
Viral capsid fold classifier detecting the jelly roll motif from protein sequence alone, using logistic regression over frozen ProtTrans embeddings.
Diffusion transformer for genetic mapping that generates and classifies bulk segregant point clouds to localize causal mutations at 0.3 Mb.
Genomics foundation model for genome-scale SNP analysis, handling imputation, phasing, ancestry, and relatedness from one 0.8B-parameter checkpoint.
Protein language model fine-tuned on yeast-display directed evolution data to score rice immune receptor variants for fungal effector binding.
Skin cancer subtype classification from H&E whole slide images, with one vision transformer reading patches at 10x, 20x, 40x and 400x.
Regulatory variant effect prediction from DNA sequence, trained only on nervous-tissue epigenomic assays to score non-coding SNPs in brain disorders.
Epitope-conditioned T cell receptor generator that writes its own in-context examples, so receptors can be designed for targets with no known binders.