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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 49–72 of the 96 closest matches
Open large language models for natural science, fine-tuned on physics, chemistry, and materials science literature with automated instruction tuning.
Histopathology foundation model trained with direct slide-level supervision on 37k whole-slide images. Averages 0.784 AUROC on 10 biomarker tasks.
Isoform-resolved variant effect prediction from DNA sequence, using graph attention over transcript splice structures across 30 human tissues.
Whole-genome somatic copy-number aberration prediction from bulk RNA-seq alone, with one pan-cancer model covering 33 tumor types.
Protein-ligand docking model for high-throughput virtual screening, predicting binding poses with graph neural networks at low computational cost.
Masked DNA language model for regulatory genomics with a motif-discovery regularizer for zero-shot TF motif recovery and variant effect prediction.
AAV capsid design platform for gene therapy that steers a peptide language model toward inserts combining receptor targeting and production fitness.
Multimodal single-cell foundation model whose multiway Transformer jointly models scRNA-seq and scATAC-seq from RNA-only, ATAC-only, or paired inputs.
Breast cancer recurrence risk read from an H&E slide and six routine clinical variables, using a frozen pan-cancer pathology encoder.
Neural ab initio reconstruction for cryo-EM and cryo-ET that jointly infers particle poses and a continuous landscape of conformational states.
Histopathology tissue detection model that segments whole-slide thumbnails in a single SAM2 forward pass, replacing patch-wise slide preprocessing.
Protein-text foundation model placing amino acid sequences and natural language in one token space for protein understanding and de novo design.
Resolution enhancement for Hi-C contact matrices, reconstructing full-depth 10 kb maps from libraries sequenced at a fraction of the read depth.
Echocardiography vision foundation model self-distilled on 20 million ultrasound images from 11 clinical centres, with swappable task decoders.
Multi-sequence MRI foundation model pretrained on 336,476 volumetric scans, ranking first on 41 of 44 downstream clinical benchmarks.
SE(3)-invariant masked autoencoder that learns protein fold representations from AlphaFold-DB structures, supporting zero-shot fold classification.
Frozen-section pathology foundation model for intraoperative diagnosis, LoRA-adapted from Virchow2 and validated in a prospective surgical study.
Foundation model for medical image registration that aligns CT and MRI across anatomies and modalities without per-pair optimization or retraining.
Antigen-specific antibody design model that conditions an ESM3 backbone on epitope geometry, then aligns CDR generation with calibrated DPO.
Single-cell RNA integration model using adversarial batch training to embed and label cells from a new study without supplying a batch ID.
Predicts 3D genome architecture directly from DNA sequence across nine scales, from 4-kb contacts up to a 256-Mb whole-chromosome window.
Chromatin interaction prediction from DNA sequence alone, calling CTCF-, RNA Pol II- and Hi-C-associated loops between open chromatin regions.