Infectious Disease

Flu & URI

Latest AI and machine learning research in flu & uri for healthcare professionals.

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U-Probe: universal agentic probe design for imaging-based spatial-omics

Probe design for fluorescence in situ hybridization (FISH) underpins spatial transcriptomics, three-...

AbLWR:A Context-Aware Listwise Ranking Framework for Antibody-Antigen Binding Affinity Prediction via Positive-Unlabeled Learning

Accurate prediction of antibody-antigen binding affinity is fundamental to therapeutic design, yet r...

Using Synthetic Data for Machine Learning-based Childhood Vaccination Prediction in Narok, Kenya

Background: Limited data utilization in low-resource settings poses a barrier to the vaccine deliver...

ViraHinter: a dual-modal artificial intelligence framework for predicting virus-host interactions

Protein-protein interactions (PPIs) between a virus and its host govern infection, replication, and ...

Predicting highly pathogenic avian influenza H5N1 outbreak risk using extreme weather and bird migration data in machine learning models

Background. Climate change is intensifying extreme weather events (EWEs) with potentially profound c...

A Machine Learning Approach to Defining and Predicting the Scale of Typhoid Fever Outbreaks

Despite improvements in access to clean water and sanitation, typhoid fever outbreaks continue to ca...

Cough activity detection for automatic tuberculosis screening

The automatic identification of cough segments in audio through the determination of start and end p...

Predictors of COVID-19 hospital outcomes: a machine learning analysis of the National COVID Cohort Collaborative

Predicting hospital outcomes for patients with severe acute respiratory infections is critical for r...

An Integrated Computational Antigen Discovery Pipeline with Hierarchical Filtering for Emerging Viral Variants

Emerging and evolving viral diseases, such as SARS-CoV-2, continue to pose significant global health...

Leveraging large language models to address common vaccination myths and misconceptions

Large language models (LLMs) are increasingly used by the public to seek health information, yet the...

t2pmhc: A Structure-Informed Graph Neural Network to predict TCR-pMHC Binding

Mapping of T cell receptors (TCRs) to their cognate MHC-presented peptides (pMHC) is central for the...

Automated Model Discovery Based on COVID-19 Epidemiologic Data

The COVID-19 pandemic has presented severe challenges in understanding and predicting the spread of ...

Genomic Evolution of SARS-CoV-2 Delta Variants Pre- and Post-Omicron Emergence using Alignment-free Machine Learning models

The SARS-CoV-2 Delta variant (B.1.617.2), initially classified as a variant of concern due to its en...

Identifying Reasons for ACEI/ARB Non-Use in CKD Using Scalable Clinical NLP with Schema-Guided LLM Augmentation

IMPORTANCE: Although angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor block...

Early Detection of Absurdity Signals in Pharmacovigilance: A Machine Learning Ensemble Approach to Identify Rare Adverse Drug Reactions

Background: Traditional pharmacovigilance methods based on biostatistical approaches systematically ...

Designing AI-programmable therapeutics with the EDEN family of foundation models

The ability to interpret, modify, and design DNA has driven many of the most significant advances in...

Predicting Anemia Among Under-Five Children in Nepal Using Machine Learning and Deep Learning

Childhood anemia remains a major public health challenge in Nepal and is associated with impaired gr...

Classification of SARS-CoV-2 Variants through The Epistatical Circos Plots with Convolutional Neural Networks

The COVID-19 pandemic has profoundly affected global health, driven by the remarkable transmissibili...

De novo design of a safe and potent respiratory syncytial virus immuno-focusing antigen

Respiratory syncytial virus (RSV) remains the leading cause of severe respiratory infections in infa...

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