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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1441–1464 of 2336 models
4B-parameter generative genome foundation model trained on assembled environmental metagenomes for microbial representation and de novo DNA design.
Autoregressive genomic foundation models from 20M to 1B parameters that solve ten DNA tasks at once and map sequences to text and images.
Self-supervised 3D vision foundation model for non-contrast head CT, pretrained on 361,663 scans to detect a broad range of intracranial disease.
Autoregressive graph generator that flattens molecules into token sequences, letting a decoder-only transformer sample valid structures in one pass.
Multimodal ECG language model pairing a specialized signal encoder with a biomedical LLM for cardiovascular disease detection and question answering.
Photoplethysmography foundation model pretrained on raw wearable signals from a field study, transferring across lab and field health tasks.
Plant genomic foundation model with a 64 kb single-nucleotide context that predicts gene structures and generates de novo plant gene sequences.
Attention architecture fusing docking scores with protein language model embeddings, so an enzyme gets a different representation per substrate.
EEG foundation model for Alzheimer's disease detection, pretrained by contrastive learning across 13 clinical EEG datasets and 2,238 subjects.
Segment Anything fine-tuned for nucleus segmentation in histopathology, supporting automatic and interactive annotation on unseen tissue images.
Cryo-ET particle picking model, an ensemble of 3D U-Nets with EfficientNet encoders that finds protein complexes in tomograms by heatmap segmentation.
Cryo-ET particle picking model that localizes six protein complexes in tomograms using an ensemble of lightweight 3D U-Nets.
Prostate cancer detection model for MRI and transrectal ultrasound, trained with patch-level contrastive learning across 4,401 patients.
Generalist cell segmentation pairing the cyto3 super-generalist model with one-click networks that denoise, deblur, and upsample microscopy images.
Cryo-ET particle picking model that averages tiny, medium, and large 3D U-Nets pretrained on simulated tomograms and fine-tuned on experimental data.
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.
Diffusion transformer for genetic mapping that generates and classifies bulk segregant point clouds to localize causal mutations at 0.3 Mb.
Viral capsid fold classifier detecting the jelly roll motif from protein sequence alone, using logistic regression over frozen ProtTrans embeddings.
Protein language model fine-tuned on yeast-display directed evolution data to score rice immune receptor variants for fungal effector binding.
Genomics foundation model for genome-scale SNP analysis, handling imputation, phasing, ancestry, and relatedness from one 0.8B-parameter checkpoint.
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.