Infectious Disease

Latest AI and machine learning research in infectious disease for healthcare professionals.

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Enhancing HCV NS3 Inhibitor Classification with Optimized Molecular Fingerprints Using Random Forest.

The classification of Hepatitis C virus (HCV) NS3 inhibitors is essential for identifying potential ...

Estimating the causal impact of non-pharmaceutical interventions on COVID-19 spread in seven EU countries via machine learning.

During the COVID-19 pandemic, Non-Pharmaceutical Interventions (NPIs) were imposed all over Europe w...

Topicwise Separable Sentence Retrieval for Medical Report Generation.

Automated radiology reporting holds immense clinical potential in alleviating the burdensome workloa...

Automation of protein crystallization scaleup via Opentrons-2 liquid handling.

In this study we present an approach for optimizing protein crystallization trials at the multi-micr...

Aggregation induced emission luminogen bacteria hybrid bionic robot for multimodal phototheranostics and immunotherapy.

Multimodal phototheranostics utilizing single molecules offer a "one-and-done" approach, presenting ...

Deep learning method for malaria parasite evaluation from microscopic blood smear.

OBJECTIVE: Malaria remains a leading cause of global morbidity and mortality, responsible for approx...

AI Enhanced explainable early prediction of blood culture positivity in neutropenic patients using clinical and hematologic parameters.

Leukemia patients who receive chemotherapy experience a decline in neutrophils and an increased risk...

Forecasting invasive mosquito abundance in the Basque Country, Spain using machine learning techniques.

BACKGROUND: Mosquito-borne diseases cause millions of deaths each year and are increasingly spreadin...

De novo design of self-assembling peptides with antimicrobial activity guided by deep learning.

Bioinspired materials based on self-assembling peptides are promising for tackling various challenge...

BERT-AmPEP60: A BERT-Based Transfer Learning Approach to Predict the Minimum Inhibitory Concentrations of Antimicrobial Peptides for and .

Antimicrobial peptides (AMPs) are a promising alternative for combating bacterial drug resistance. W...

Circulating lncRNAs as biomarkers for severe dengue using a machine learning approach.

OBJECTIVES: Dengue virus (DENV) infection is a significant global health concern, causing severe mor...

SERS-based approaches in the investigation of bacterial metabolism, antibiotic resistance, and species identification.

Surface-enhanced Raman scattering (SERS) is an inelastic scattering phenomenon that occurs when phot...

Explainable machine learning models enhance prediction of PFAS bioactivity using quantitative molecular surface analysis-derived representation.

The extensive use of per- and polyfluoroalkyl substances (PFAS) in industrial and consumer products ...

Exploration of Novel Antimicrobial Agents against Foodborne Pathogens via a Deep Learning Approach.

The emergence of antibiotic-resistant bacteria poses a severe threat to food safety and human health...

Enhancing yeast cell tracking with a time-symmetric deep learning approach.

Accurate tracking of live cells using video microscopy recordings remains a challenging task for pop...

Exploring the repository of de novo-designed bifunctional antimicrobial peptides through deep learning.

Antimicrobial peptides (AMPs) are attractive candidates to combat antibiotic resistance for their ca...

Capillariid diversity in archaeological material from the New and the Old World: clustering and artificial intelligence approaches.

BACKGROUND: Capillariid nematode eggs have been reported in archaeological material in both the New ...

Forecasting dengue across Brazil with LSTM neural networks and SHAP-driven lagged climate and spatial effects.

BACKGROUND: Dengue fever is a mosquito-borne viral disease that poses significant health risks and s...

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