Infectious Disease

Deep learning revealed the distribution and evolution patterns for invertible promoters across bacterial lineages.

Invertible promoters (invertons) are crucial regulatory elements in bacteria, facilitating gene expression changes under stress. Despite their importance, their prevalence and the range of regulated gene functions are largely unknown. We introduced DeepInverton, a deep learning model that identifies invertons across a broad phylogenetic spectrum without using sequencing reads. By analyzing 68 733 ...

[Investigation of the impact of the deep learning based CT fractional flow reserve on clinical decision-making and long-term prognosis in patients with obstructive coronary heart disease].

To investigate the impact of the deep-learning-based CT fractional flow reserve (CT-FFR) on clinical decision-making and long-term prognosis in patients with obstructive coronary heart disease. In this single-center retrospective cohort study, consecutive patients with obstructive coronary heart disease (with at least one stenosis≥50%) on their first coronary computed tomography angiography (CCT...

[Study on predicting new onset heart failure events in patients with hypertrophic cardiomyopathy using machine learning algorithms based on clinical and magnetic resonance features].

To explore the value of predicting new-onset heart failure events in patients with hypertrophic cardiomyopathy (HCM) using clinical and cardiac magne...

A Decision Support System for daily scheduling and routing of home healthcare workers with a lunch break consideration

This study examines a home healthcare scheduling and routing problem (HHSRP) with a lunch break requirement. This problem especially consists of lun...

ML-Based Framework to Predict the Severity of the Symptomatology in Patients with Post-Acute COVID-19 Syndrome.

The paper describes a cohort of patients with post-acute COVID-19 syndrome, evaluated for the first time between week 3 and week 12 from the onset of ...

Predicting phage-host interactions via feature augmentation and regional graph convolution.

Identifying phage-host interactions (PHIs) is a crucial step in developing phage therapy, which is the promising solution to addressing the issue of a...

Machine learning-enabled virtual screening indicates the anti-tuberculosis activity of aldoxorubicin and quarfloxin with verification by molecular docking, molecular dynamics simulations, and biological evaluations.

Drug resistance in Mycobacterium tuberculosis (Mtb) is a significant challenge in the control and treatment of tuberculosis, making efforts to combat ...

VirDetect-AI: a residual and convolutional neural network-based metagenomic tool for eukaryotic viral protein identification.

This study addresses the challenging task of identifying viruses within metagenomic data, which encompasses a broad array of biological samples, inclu...

Primary care research on hypertension: A bibliometric analysis using machine-learning.

Hypertension is one of the most important chronic diseases worldwide. Hypertension is a critical condition encountered frequently in daily life, formi...

Deep learning-based design and experimental validation of a medicine-like human antibody library.

Antibody generation requires the use of one or more time-consuming methods, namely animal immunization, and in vitro display technologies. However, th...

DeepPFP: a multi-task-aware architecture for protein function prediction.

Deriving protein function from protein sequences poses a significant challenge due to the intricate relationship between sequence and function. Deep l...

Applications of Machine Learning on Electronic Health Record Data to Combat Antibiotic Resistance.

There is growing excitement about the clinical use of artificial intelligence and machine learning (ML) technologies. Advancements in computing and th...

Identifying Virulence Determinants In Pathogenic Mycobacteria Via Changes In Host Cell Mitochondrial Morphology

The goal of this study is to develop a computational model of the progression of changes in mitochondrial phenotype resulting from infection with pa...

Prediction of teicoplanin plasma concentration in critically ill patients: a combination of machine learning and population pharmacokinetics.

BACKGROUND: Teicoplanin has been widely used in patients with infections caused by Staphylococcus aureus, especially for critically ill patients. The ...

Multiplex Imaging Analysis in Pathology: a Comprehensive Review on Analytical Approaches and Digital Toolkits

Conventional histopathology has long been essential for disease diagnosis, relying on visual inspection of tissue sections. Immunohistochemistry aid...

Revealing transparency gaps in publicly available COVID-19 datasets used for medical artificial intelligence development-a systematic review.

During the COVID-19 pandemic, artificial intelligence (AI) models were created to address health-care resource constraints. Previous research shows th...

Integrating Contact Tracing Data to Enhance Outbreak Phylodynamic Inference: A Deep Learning Approach.

Phylodynamics is central to understanding infectious disease dynamics through the integration of genomic and epidemiological data. Despite advancement...

Psychological predictors of socioeconomic resilience amidst the COVID-19 pandemic: Evidence from machine learning.

What predicts cross-country differences in the recovery of socioeconomic activity from the COVID-19 pandemic? To answer this question, we examined how...

Can machine learning models improve the prediction of surgical site infection in abdominal surgery than traditional statistical models?

OBJECTIVE: To externally validate by revision and update the study on the efficacy of nosocomial infection control (SENIC) model of surgical site infe...

Machine learning-driven in-hospital mortality prediction in HIV/AIDS patients with infection: a single-centred retrospective study.

() is a widely disseminated betaherpesvirus that typically induces latant infections. In immunocompromised populations, especially transplant and HI...