Infectious Disease

Fleming: An AI Agent for Antibiotic Discovery in Mycobacterium Tuberculosis

Antibiotic development is challenged by high costs and failure rates. Artificial intelligence (AI) holds promise to overcome these challenges by predicting inhibitory properties of novel compounds, generating new candidates, and contextualizing property predictions in the biological background. Fleming is an integrative AI agent that explores novel chemical space to identify lead compounds meeting...

Cough activity detection for automatic tuberculosis screening

The automatic identification of cough segments in audio through the determination of start and end points is pivotal to building scalable screening tools in health technologies for pulmonary related diseases. We propose the application of two current pre-trained architectures to the task of cough activity detection. A dataset of recordings containing cough from patients symptomatic for tuberculosi...

abx_amr_simulator: A simulation environment for antibiotic prescribing policy optimization under antimicrobial resistance

Antimicrobial resistance (AMR) poses a global health threat, reducing the effectiveness of antibiotics and complicating clinical decision-making. To a...

Bacterial proteome foundation model enhances functional prediction from enzymes to ecological interactions

Bacteria play fundamental roles in ecosystems, human health, and biotechnology. Although bacterial genome sequencing data have accumulated rapidly ove...

Hospital AI and Robotics Adoption, Access Inequality, and County Mortality: A National Study Across 3,143 U.S. Counties

Hospital artificial intelligence (AI) and robotics are spreading unevenly across the United States, yet national evidence on how these technologies ar...

Machine Unlearning for GDPR Right-to-Erasure in Antimicrobial Resistance Prediction Models

Objective. Healthcare machine learning models trained on patient data must comply with the General Data Protection Regulation (GDPR) right to erasure ...

A Rule-Based Machine Learning Model for Predicting Virological Failure Among Children Living With HIV in Malawi

Malawi's HIV treatment monitoring system faces serious challenges because of a shortage of experts and reliance on viral load testing every 3 to 12 mo...

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 risk stratification and resource planning, yet hete...

Optimising antibiotic switching via forecasting of patient physiology

Timely transition from intravenous (IV) to oral antibiotic therapy shortens hospital stays, reduces catheter-related infections, and lowers healthcare...

REMAG: recovery of eukaryotic genomes from metagenomic data using contrastive learning

Metagenome-assembled genomes (MAGs) are central to exploring microbial communities. Yet, despite the relevance of protists and fungi to diverse ecosys...

AMR-CCR: Anchored Modular Retrieval for Continual Chinese Character Recognition

Ancient Chinese character recognition is a core capability for cultural heritage digitization, yet real-world workflows are inherently non-stationary:...

Deep Learning-based Differentiation of Drug-induced Liver Injury and Autoimmune Hepatitis: A Pathological and Computational Approach

Drug-induced liver injury (DILI) is an acute inflammatory liver disease caused not only by prescription and over-the-counter medications but also by h...

dAMN: a genome scale neural-mechanistic hybrid model to predict bacterial growth dynamics

This study presents dAMN, a hybrid neural-mechanistic model that integrates neural networks with genome-scale dynamic flux balance analysis (dFBA) to ...

Characterizing Autonomic Dysfunction during Resuscitation in Sepsis using Multiscale Entropy

Rationale Autonomic dysfunction is a hallmark of sepsis pathophysiology, yet its quantification remains challenging. Multiscale entropy (MSE) derived ...

A high-throughput method for measuring fungal growth rate on solid media using automated imaging and deep learning

Measuring the growth rate of filamentous fungi is an essential phenotype assay in fungal biology, enabling the comparison of nutrient-related fitness ...

SeekRBP: Leveraging Sequence-Structure Integration with Reinforcement Learning for Receptor-Binding Protein Identification

Motivation: Receptor-binding proteins (RBPs) initiate viral infection and determine host specificity, serving as key targets for phage engineering and...

SurvHTE-Bench: A Benchmark for Heterogeneous Treatment Effect Estimation in Survival Analysis

Estimating heterogeneous treatment effects (HTEs) from right-censored survival data is critical in high-stakes applications such as precision medicine...

Two-step deep-learning candidemia prediction model using two large time-sequence electronic health datasets

Background Candidemia is a rare but life-threatening bloodstream infection that remains difficult to predict using conventional risk stratification ap...

Multistate Animal-Contact-Related Nontyphoidal Salmonella enterica Outbreaks in the United States, 2009-2022: Network and Machine Learning Analyses of Exposure Sources, Settings, and Serovars

Background: Nontyphoidal Salmonella enterica (NTS) is a major public-health threat in the United States of America (U.S.). Evaluating associations bet...

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 challenges, underscoring the urgent need for rapi...