Harvard Medical School

Transformer that predicts protein-protein interactions at residue resolution, spanning mutations, PTMs, peptide-MHC binding, and disease variants.

RNA language model that predicts secondary structure of internal ribosome entry sites from sequence alone, trained on roughly 50,000 IRES sequences.

Vision-omics finetuning that aligns pathology foundation models with spatial transcriptomics so morphology features predict local gene expression.

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

Native 3D vision transformer self-supervised on unlabeled fluorescence microscopy volumes, segmenting subcellular structures without voxel labels.

Siamese protein language model whose embedding distances approximate TM-score and lDDT, enabling alignment-free protein structure comparison.

Predicts single-cell scRNA-seq coverage and scATAC-seq insertion profiles from DNA sequence, adapting the Borzoi trunk with a cell-specific decoder.

Modality-agnostic foundation model for human brain imaging that runs five core neuroimaging tasks across uncalibrated CT and MRI without retraining.

Attention-based multiple instance learning heads for whole-slide pathology, pretrained on a 108-way pan-cancer slide classification task.

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

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

Multimodal foundation model integrating protein sequence, structure, and natural language to model and generate protein phenotypes across scales.

Self-supervised vision foundation model for structural brain MRI, providing a reusable encoder for brain age, survival, and image classification.

Slide-level pathology foundation model turning whole-slide images into reusable embeddings for classification, retrieval, and report generation.

Conditional autoregressive genomic language model trained on 13.6M mammalian promoters, scoring promoter variants, including indels, zero-shot.

Convolutional ECG foundation model trained on expert annotations spanning 150 diagnostic categories, with 12-lead and single-lead wearable variants.

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

Slide-level pathology foundation model that learns whole-slide embeddings by aligning multiple stains of the same tissue during pretraining.

Multimodal vision-language copilot for pathology that answers open-ended questions about histology images and reasons about differential diagnoses.

DeepLabV3 segmentation model that separates tissue from glass background in H&E and IHC whole-slide images, as used by the HEST-Library.

Computational pathology foundation model (ViT-L/16, DINOv2) pretrained on over 100 million H&E tiles from more than 100,000 whole-slide images.

Histopathology vision-language foundation model pretrained on 1.17 million image-caption pairs with contrastive and captioning objectives.

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

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

Medical image segmentation model that solves unseen segmentation tasks in context from a few labeled examples, with no retraining or fine-tuning.