Infectious Disease

Improved prediction of virus-human protein-protein interactions by incorporating network topology and viral molecular mimicry

The protein-protein interactions (PPIs) between viruses and human play crucial roles in viral infections. Although numerous computational approaches have been proposed for predicting virus-human PPIs, their performances remain suboptimal and may be overestimated due to the lack of benchmark dataset. To address these limitations, we first constructed a carefully curated benchmark dataset, ensuring ...

NN-Assisted Image Analysis for Quantifying Intracellular Trypanosoma cruzi Infection

Trypanosoma cruzi infection remains a central, yet methodologically challenging step in Chagas disease research and early-stage drug discovery. Current approaches largely rely on manual microscopy-based counting or on genetically modified parasites, both of which present limitations in scalability, reproducibility, or accessibility. Here, we developed and validated a neural network (NN)-based pipe...

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 their reliability in addressing common vaccine myths ...

Can Machine Learning Algorithms use Contextual Factors to Detect Unwarranted Clinical Variation from Electronic Health Record Encounter Data during the Treatment of Children Diagnosed with Acute Viral Pharyngitis

Rationale, Aims and Objectives: Unwarranted clinical variation (UCV) in patient care often arises from contextual factors and contributes to increased...

The Causal Impact of Natural Language Processing-Driven Clinical Decision Support on Sepsis Mortality in England: An Augmented Synthetic Control Analysis of NHS Trust-Level Data

Background: Sepsis remains a leading cause of preventable hospital mortality in England, with NHS England reporting over 48,000 sepsis-related deaths ...

Explainable AI for end-to-end pathogen target discovery and molecular design

Drug discovery is often constrained by target identification, a bottleneck especially acute in antimicrobial development and the fight against emergin...

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 development of precision immunotherapies and vacc...

Eubiota: Modular Agentic AI for Autonomous Discovery in the Gut Microbiome

The gut microbiome regulates many aspects of human biology, including immunity and inflammatory diseases, yet mechanistic discovery and translation re...

Cross hybridization Inference for Phylogenetic Resolution (CIPHR)-FISH enables microbiome imaging with strain level taxonomic resolution

The spatial organization of microbial communities is a critical determinant of host-microbe interactions, yet species-level mapping remains challengin...

Identifying severe COVID-19 risk variants modulating enhancer reporter activity in lung cells

Common genetic variants contribute to risk for complex human diseases. However, despite thousands of associations, variants modulating disease risk an...

Can Agents Distinguish Visually Hard-to-Separate Diseases in a Zero-Shot Setting? A Pilot Study

The rapid progress of multimodal large language models (MLLMs) has led to increasing interest in agent-based systems. While most prior work in medical...

Predicting Multi-Drug Resistance in Bacterial Isolates Through Performance Comparison and LIME-based Interpretation of Classification Models

The rise of Antimicrobial Resistance, particularly Multi-Drug Resistance (MDR), presents a critical challenge for clinical decision-making due to limi...

Data-Driven Hybrid Model of SARIMA-CNNAR For Tuberculosis Incidence Time Series Analysis in Nepal

Abstract Background Tuberculosis (TB) remains a major public health challenge in Nepal, with incidence rates substantially higher than global estimate...

Automated Model Discovery Based on COVID-19 Epidemiologic Data

The COVID-19 pandemic has presented severe challenges in understanding and predicting the spread of infectious diseases, necessitating innovative appr...

FLIM Networks with Bag of Feature Points

Convolutional networks require extensive image annotation, which can be costly and time-consuming. Feature Learning from Image Markers (FLIM) tackles ...

Sequential Counterfactual Inference for Temporal Clinical Data: Addressing the Time Traveler Dilemma

Counterfactual inference enables clinicians to ask "what if" questions about patient outcomes, but standard methods assume feature independence and si...

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 enhanced transmissibility and vaccine-escape mutatio...

Deep learning extracts MoA-specific signatures from high-throughput images of chemically and genetically perturbed Corynebacteria

Tuberculosis (TB) is the worldwide leading infectious killer due to a single pathogen and increasing antimicrobial resistance (AMR) makes it imperativ...

Hierarchical Multi-Omics Trajectory Prediction forFecal Microbiota Transplantation: A Novel MachineLearning Framework for Small-Sample LongitudinalMulti-Omics Integration

Fecal microbiota transplantation (FMT) has emerged as a highly effective treatment for recurrent Clostridioides difficile infection and is being activ...

Redefining the Down-Sampling Scheme of U-Net for Precision Biomedical Image Segmentation

U-Net architectures have been instrumental in advancing biomedical image segmentation (BIS) but often struggle with capturing long-range information. ...