Harvard Medical School

Transformer framework that models protein-protein interactions at residue resolution, generalizing zero-shot to unseen MHC alleles and sequence-neutral PTMs from one fixed checkpoint.

An RNA language model trained by self-supervised masked-nucleotide prediction on ~50,000 IRES sequences that predicts secondary-structure features rivaling experimental chemical probing.

A full-atom SE(3)-equivariant diffusion model that inpaints binding interfaces to design proteins that bind DNA, RNA, and small molecules.

A native 3D vision transformer self-supervised on unlabeled fluorescence microscopy volumes that generalizes to unseen object classes without retraining or voxel annotations.

Predicts single-cell-resolution scRNA-seq coverage and scATAC-seq insertion profiles directly from DNA sequence by adapting the Borzoi predictor with a cell-specific decoder.

A modality-agnostic, multi-task foundation model for human brain imaging that runs five core tasks across uncalibrated CT and MRI without retraining.

A vision-language foundation model for precision oncology that pretrains on 50M pathology images and 1B text tokens via unified masked modeling.

A self-supervised vision foundation model for structural brain MRI that produces general-purpose features adaptable to diverse downstream clinical and neuroscience tasks.

A 1D convolutional foundation model for electrocardiogram analysis, trained on over 10 million expert-annotated recordings across 150 diagnostic categories.

A weakly supervised pathology foundation model pretrained on 60,530 whole-slide images across 19 anatomical sites for cancer detection, prognosis, and molecular prediction.

A multimodal vision-language copilot for human pathology that analyzes histology images and answers diverse pathology queries in natural language.

Self-supervised pathology foundation model (ViT-L/16, DINOv2) pretrained on 100M+ H&E tiles from 100,000+ whole-slide images. State-of-the-art on 34 pathology tasks.

Vision-language foundation model for computational pathology, pretrained on 1.17M histopathology image-caption pairs with contrastive and captioning objectives.

A self-supervised 3D CT foundation model that extracts general-purpose tumor representations for cancer imaging biomarker discovery across diverse downstream tasks.

A multimodal medical vision-language model that performs few-shot generative visual question answering over medical images and text.

An in-context learning model that segments unseen medical imaging tasks from a few labeled examples, with no retraining or fine-tuning.