Latest AI and machine learning research in covid-19 for healthcare professionals.
Antibodies raised against human targets often fail to recognize their animal orthologs, limiting preclinical evaluation in relevant models. We developed a Deep Mutational Scanning (DMS)-coupled deep learning strategy to engineer potent cross-reactive antibodies with minimal sequence divergence. Starting from C4, a fully human anti-PD-L1 antibody with weak recognition of murine PD-L1, DMS identifie...
The rapid expansion of chimeric antigen receptor (CAR) T cell studies has produced a fragmented evidence landscape linking publications, repository accessions, patient metadata and mechanistic observations. Here we present BioPathfinder, a multi-agent discovery engine for CAR-T research evidence construction, hypothesis generation and validation planning. Unlike existing LLM-based and agentic appr...
Background Unsupervised machine learning has become a cornerstone of computational phenotyping across clinical medicine, genomics, imaging, and multi-...
Recent diffusion-based virtual try-on (VTON) methods remain limited by their reliance on segmentation masks, insufficient preservation of fine-grained...
Clinical AI models can expose patients to harm when adversarial vulnerabilities go undetected, yet formal security auditing requires statistical exper...
While 3D Gaussian Editing (3DGE) has seen substantial progress, text-driven 3D human garment editing remains largely underexplored. Existing 3DGE work...
The rapid evolution of image generation has produced numerous within-family variants, making source-model attribution of suspect images increasingly i...
Imaging spectrometers increasingly distribute source-resolved methane plume products in which the plume mask, integrated mass enhancement (IME), plume...
The Segment Anything Model (SAM) has demonstrated strong generalizability across a variety of segmentation tasks. However, SAM often struggles in situ...
Background General-purpose large language models increasingly encounter emotional and therapy-like conversation, yet are not developed or evaluated as...
Discovering functional peptides across vast sequence space remains a formidable challenge, particularly when experimental training data is scarce. We ...
Low-power event-based Analog Front-Ends (AFEs) are essential for building efficient, end-to-end neuromorphic signal processing systems. In this paper,...
X-ray tomography enables nondestructive characterization of material microstructures, while advances in micro-CT imaging have accelerated volumetric d...
Foundation models such as CLIP have enabled open-vocabulary object detectors that generalise to novel categories via vision-language similarity. Howev...
Conditional diffusion models can generate anatomically plausible medical ultrasound (US) images, but anatomical plausibility alone does not ensure rea...
Saliency maps are most useful when they identify the image regions that are sufficient to preserve a model's behaviour. We introduce SEAMS, a sufficie...
Deep learning models that predict molecular phenotypes directly from DNA sequence offer a powerful framework for interpreting genomic variation. Recen...
High-resolution 3D imaging is an important strategy for visualizing and analysing complex skeletal tissue architecture and the bone marrow microenviro...
Structural variants are a major source of genomic variation and contribute to human disease and evolution through diverse mechanisms, yet their functi...
Accurate immune receptor design requires modeling the coupled variation of amino-acid sequence, full-atom conformation, and target-binding geometry ac...