University of Oxford

Fine-tuned Enformer derivative that predicts discrete, interpretable cis-regulatory element class annotations (enhancer, promoter, insulator) directly from DNA sequence across human cell types.

Pretrained antibody structure predictor that outputs full paired heavy/light 3D structures faster than protein language models generate embeddings.

A multimodal transformer foundation model for ECG, PPG, and clinical text, pretrained on heterogeneous cardiac data from ~1.7 million individuals.

A 3D MRI organ segmentation foundation model based on Swin-UNETR, trained on the UKBOB dataset of 1.37 billion labeled masks across 72 anatomical structures.

A 2B-parameter medical vision-language model that uses reinforcement learning (GRPO) to produce explicit, human-interpretable reasoning for radiology visual question answering.

Multimodal, multidomain, multilingual medical foundation model that performs zero-shot clinical diagnosis and report generation from chest X-ray and CT images across English and Chinese.

SAM2-based foundation model that segments 2D and 3D medical images by treating volumes and image sets as video object tracking.

Self-supervised CNN pretrained on 700,000 person-days of UK Biobank accelerometer data for human activity recognition, transferable across devices and cohorts.

Codon-level BERT model that captures genomic signals invisible to amino acid models, outperforming billion-parameter PLMs with just 86M parameters.

Text-informed self-supervised vision-language pretraining for 3D CT volumes, enabling zero-shot classification, retrieval, report generation, and segmentation.

Antibody-specific language model trained on the OAS database for restoring missing residues and generating high-quality sequence representations.