Latest AI and machine learning research in infectious disease for healthcare professionals.
Eukaryotic genome annotation is currently bottlenecked by limitations in the generality, scalability and accuracy of computational methods. Deep learning approaches have recently achieved large improvements in ab initio gene prediction accuracy. We extend the deep learning-based ab initio gene predictor Tiberius beyond mammals by training lineage-specific models for Mesangiospermae, Fungi, Vertebr...
Forecasting infectious disease incidence can provide important information to guide public health planning, yet is difficult because epidemic dynamics are complex. Current mechanistic and statistical approaches often struggle to capture multimodal uncertainty or emergent trends. Influpaint adapts denoising diffusion probabilistic models to epidemic forecasting. By encoding influenza seasons as spa...
Background Although Kenya's HIV programme has long prioritized high-burden counties for intensified paediatric interventions, a critical evidence gap ...
Identifying robust gene expression signatures from transcriptomic studies with small sample sizes remains one of the most persistent challenges in com...
Sepsis is a leading cause of in-hospital mortality, yet systematically evaluating temporal adherence to the Surviving Sepsis Campaign (SSC) bundle acr...
Tuberculosis (TB) is prevalent in Uganda and overlaps with a high rate of HIV/TB coinfection. While nearly all hospital-based TB cases in Kampala, the...
Understanding how cells respond to perturbations like viral infections requires models capturing coordinated gene dynamics. However, current gene expr...
This study developed a large language model (LLM)-based solution to identify people at HIV risk using electronic health records. We transformed struct...
Generalizable protein-expression prediction can accelerate protein engineering, inform disease mechanisms, and help optimize heterologous recombinant ...
Multimodal clinical records contain structured measurements and clinical notes recorded over time, offering rich temporal information about the evolut...
Timely and interpretable early warning of sepsis remains a major clinical challenge due to the complex temporal dynamics of physiological deterioratio...
Assessing chronic wound infection from photographs is challenging because visual appearance varies across wound etiologies, anatomical locations, and ...
Automated bacterial colony counting from images is an important technique to obtain data required for the development of vaccines and antibiotics. How...
Bacterial identification, antimicrobial resistance prediction, and strain typification are critical tasks in clinical microbiology, essential for guid...
Stroke-associated pneumonia (SAP) is a common, severe complication in acute ischemic stroke (AIS) patients receiving bridging therapy (intravenous thr...
Accurate prediction of future risk and disease progression in sepsis is clinically important for early warning and timely intervention in intensive ca...
Significant progress has been made in detecting synthetic images, however most existing approaches operate on a single image instance and overlook a k...
Probe design for fluorescence in situ hybridization (FISH) underpins spatial transcriptomics, three-dimensional genome studies, and clinical diagnosti...
Background: Datasets related to infectious diseases are essential for public health decision-making, yet their reuse remains limited by persistent bar...
Fall detection in elderly care requires not only accurate classification but also reliable explanations that clinicians can trust. However, existing p...