Latest AI and machine learning research in covid-19 for healthcare professionals.
High-quality datasets that span broad sequence diversity are essential for understanding protein sequence-function relationships beyond local mutational landscapes. Here, we applied Growth-based Quantitative Sequencing (GROQ-seq) to measure function across an 11,722 member protease library, comprised of natural homologs and AI-shrunken variants. This library spans vast sequence diversity, with Lev...
Shadows cast by terrain and tall structures remain a major obstacle for high-resolution satellite image analysis, degrading classification, detection, and 3D reconstruction performance. Public resources offering geometry-consistent paired shadow/shadow-free satellite imagery are essentially missing, and most Earth-observation datasets are designed for shadow detection or 3D modelling rather than r...
Visual affordances identify regions in an image with potential interactions, offering a novel paradigm for scene understanding. Recognizing affordance...
Predicting microsatellite instability (MSI) status from routine hematoxylin and eosin (H&E) whole slide images (WSIs) offers a practical alternative t...
The incidence of the vast majority of neurodegenerative, cancer, and metabolic diseases generally increases exponentially with age. In large-scale bio...
Single-point supervised infrared small target detection (IRSTD) drastically reduces dense annotation costs. Current state-of-the-art (SOTA) methods ac...
Prior expectations guide sensorimotor behavior when sensory information is uncertain, yet the cellular mechanisms underlying this integration remain e...
SAM2 produces high-quality zero-shot segmentation on natural images, but applying it to large remote sensing scenes exposes two problems: (1) its mask...
Machine learning is increasingly used to guide protein engineering by predicting how mutations affect desired properties. Recent models for the turnov...
Background: Alcohol-induced osteonecrosis of the femoral head (AIONFH) is an orthopedic disorder from chronic alcohol abuse, characterized by disrupte...
Aims To systematically evaluate how artificial intelligence and machine-learning (AI/ML) methods are applied to cardiac biomarkers after myocardial in...
Spatial transcriptomics (ST) is a cutting-edge technology that measures gene expression while preserving spatial context and generating pathology-grad...
Purpose: Rapid and reliable diagnostic tools are crucial for managing respiratory diseases like COVID-19, where chest X-ray analysis coupled with arti...
Diffusion models have achieved remarkable success in synthesizing complex static and temporal visuals, a breakthrough largely driven by Classifier-Fre...
Deep learning methods have proved highly effective for classification and image recognition problems. In this paper, we ask whether this success can b...
Objective: To test whether machine learning (ML) models trained on tidal breathing flow time series can discriminate between individuals with and with...
Background Timely detection is crucial to improve outcomes in patients with cardiac amyloidosis (CA) by initiation of life-saving treatments. Although...
Typographic prompt injection exploits vision language models' (VLMs) ability to read text rendered in images, posing a growing threat as VLMs power au...
Variants of uncertain significance (VUS) in USH2A represent a critical interpretive challenge in inherited retinal disease, with over 70% of ClinVar s...
Nanobodies exhibit antigen binding a[ffi]nities of the same order as those of antibodies, which, along with their small size and unique structural cha...