Stanford University

Generative framework that learns a developmental vector field from scRNA-seq snapshots, coupling flow matching with molecular RNA kinetics.

Graph foundation model for fMRI brain networks, pretrained across 27 datasets with graph and language prompts for zero-shot disorder classification.

Multimodal Q-former that fuses DNA sequence, gene context, protein function, and text for zero-shot variant interpretation with a frozen LLM.

Reasoning-guided foundation model for de novo antibody CDR design, pairing a multimodal LLM understanding expert with a Boltz-1 diffusion expert.

110M-parameter RNA language model that designs sequences from secondary structure, motif, and Gene Ontology constraints via discrete diffusion.

Generative pipeline for epitope-targeted de novo antibody (nanobody) CDR design that yields nanomolar binders from only dozens of designs per antigen.

Spatial transcriptomics foundation model pairing gene expression with H&E histology for spatial domain discovery and clinical outcome prediction.

Diffusion-transformer pathology model embedding H&E histology, RNA profiles, and clinical text in a latent space for zero-shot cross-modal synthesis.

Graph attention model that learns context-aware protein embeddings from protein-protein interaction, co-expression, and tissue association networks.

Masked DNA language model for regulatory genomics with a motif-discovery regularizer for zero-shot TF motif recovery and variant effect prediction.

Graph-attention model that predicts A-to-I RNA editing from sequence and secondary structure, treating RNA as a graph with base-pairing edges.

Spatial transcriptomics foundation model built on a lightweight graph convolutional network and trained by masked central-spot prediction.

Single-cell foundation model using tabular attention over context cells to predict responses to arbitrary perturbations without fine-tuning.

Flow-matching model for therapeutic peptide design that co-designs sequence, structure, and molecular surface to disrupt protein-protein interactions.

3D vision-language foundation model for abdominal CT, pretrained on scans, radiology reports, and EHR codes for zero-shot interpretation.

Tissue imaging foundation model pretrained on matched H&E histology and spatial proteomics for cross-modal inference and zero-shot retrieval.

Hierarchical single-cell foundation model that turns scRNA-seq profiles into zero-shot donor-level embeddings for disease and biomarker prediction.

Denoising diffusion bridge model for peptide binder design that generates ligand surfaces and backbones complementary to a target receptor surface.

All-atom protein representation model that learns from each residue's strictly local atomic neighborhood, capturing side-chain geometry and chemistry.

Potts-model inverse folding that conditions on a structural ensemble rather than a single backbone, improving designability and self-consistency.

Spatial proteomics foundation model, marker-aware and panel-agnostic, pretrained on 47 million multiplexed tissue-imaging patches from 175 markers.

General medical vision-language model trained with reinforcement learning to reason step by step over medical images for diagnosis and visual QA.

Vision-language foundation model for precision oncology, pretrained on 50M pathology images and 1B text tokens via unified masked modeling.

Vision transformers trained on Human Protein Atlas fluorescence microscopy for subcellular protein localization and cell morphology representation.

Weakly supervised histopathology foundation model pretrained on 60,530 whole-slide images for cancer detection, prognosis, and molecular prediction.

Open-source, lightweight generalist vision-language foundation model for diverse biomedical imaging and text tasks.

Medical vision-language model trained on the MedTrinity-25M dataset, answering questions and generating text about radiology and histology images.

Semi-supervised cryo-ET segmentation framework that adapts DINOv2 vision transformers for 3D organelle annotation using sparse 2D slice labels.

Multi-modal foundation model for sleep analysis, learning joint representations across brain, cardiac, and respiratory polysomnography signals.

Self-supervised 3D CT foundation model that extracts general-purpose tumor representations for cancer imaging biomarker discovery and prognosis.

RNA foundation model trained on chemical-mapping data from millions of sequences, predicting reactivity, secondary structure, and degradation.

Instruction-tuned vision-language foundation model for chest X-ray interpretation, with 8 billion parameters spanning eight clinical task types.

Single-cell foundation model producing species-agnostic cell embeddings by representing genes through frozen ESM-2 protein language model embeddings.

Vision-language foundation model for pathology, fine-tuned from CLIP on 208,414 image-text pairs for zero-shot classification and image retrieval.

Multimodal medical vision-language model for few-shot visual question answering, learning new imaging tasks from in-context examples at inference.

Genomic foundation model built on the Hyena operator, processing DNA at single-nucleotide resolution with context windows up to 1 million tokens.

Zero-shot antibody affinity maturation using ESM pseudolikelihood scoring. Improves binding up to 160-fold with no antigen-specific training data.

Text-conditioned latent diffusion model that generates synthetic chest X-rays from free-form radiology prompts by adapting Stable Diffusion.

Self-supervised vision-language model for zero-shot detection of chest X-ray pathologies, trained on image-report pairs without explicit labels.