Infectious Disease

Development and validation of an interpretable machine learning model for predicting the risk of hepatocellular carcinoma in patients with chronic hepatitis B: a case-control study.

BACKGROUND: The aim of this study was to develop and internally validate an interpretable machine learning (ML) model for predicting the risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB) infection.

Predicting periprosthetic joint infection in primary total knee arthroplasty: a machine learning model integrating preoperative and perioperative risk factors.

BACKGROUND: Periprosthetic joint infection leads to significant morbidity and mortality after total knee arthroplasty. Preoperative and perioperative risk prediction and assessment tools are lacking in Asia. This study developed the first machine learning model for individualized prediction of periprosthetic joint infection following primary total knee arthroplasty in this demographic.

Predicting the immunological nonresponse to antiretroviral therapy in people living with HIV: a machine learning-based multicenter large-scale study.

BACKGROUND: Although highly active antiretroviral therapy (HAART) has greatly enhanced the prognosis for people living with HIV (PLWH), some individua...

Prediction of new HIV infection in men who have sex with men based on machine learning: secondary analysis of a prospective cohort study from Western China.

OBJECTIVE: This study aimed to construct a model based on machine learning to predict new HIV infections in HIV-negative men who have sex with men (MS...

deep-Sep: a deep learning-based method for fast and accurate prediction of selenoprotein genes in bacteria.

Selenoproteins are a special group of proteins with major roles in cellular antioxidant defense. They contain the 21st amino acid selenocysteine (Sec)...

ASVirus: A Comprehensive Knowledgebase for the Viral Alternative Splicing.

Viruses are significant human pathogens responsible for pandemic outbreaks and seasonal epidemics. Viral infectious diseases impose a devastating glob...

Enhancing Bacterial Phenotype Classification Through the Integration of Autogating and Automated Machine Learning in Flow Cytometric Analysis.

Although flow cytometry produces reliable results, the data processing from gating to fingerprinting is prone to subjective bias. Here, we integrated ...

Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.

INTRODUCTION: Sepsis, a critical medical condition resulting from an irregular immune response to infection, leads to life-threatening organ dysfuncti...

Effect of training sample size, image resolution and epochs on filamentous and floc-forming bacteria classification using machine learning.

Computer vision techniques can expedite the detection of bacterial growth in wastewater treatment plants and alleviate some of the shortcomings associ...

Seeking Correlation Among Porin Permeabilities and Minimum Inhibitory Concentrations Through Machine Learning: A Promising Route to the Essential Molecular Descriptors.

Developing effective antibiotics against Gram-negative bacteria remains challenging due to their protective outer membrane. With this study, we invest...

Multiplexed engineering of cytochrome P450 enzymes for promoting terpenoid synthesis in Saccharomyces cerevisiae cell factories: A review.

Terpenoids, also known as isoprenoids, represent the largest and most structurally diverse family of natural products, and their biosynthesis is close...

PM concentration prediction using machine learning algorithms: an approach to virtual monitoring stations.

One of the most important pollutants is PM, which is particularly important to monitor pollutant levels to keep the pollutant concentration under cont...

Interpretable Multimodal Fusion Model for Bridged Histology and Genomics Survival Prediction in Pan-Cancer.

Understanding the prognosis of cancer patients is crucial for enabling precise diagnosis and treatment by clinical practitioners. Multimodal fusion mo...

Systematic review on visual aid technologies for surgical assistant robotic devices.

This review comprehensively analyzes the modern literature on including visual aids in diverse surgical assistant robotic systems. The review consider...

Diagnostic Performance of Artificial Intelligence-Based Methods for Tuberculosis Detection: Systematic Review.

BACKGROUND: Tuberculosis (TB) remains a significant health concern, contributing to the highest mortality among infectious diseases worldwide. However...

Skin lesion segmentation with a multiscale input fusion U-Net incorporating Res2-SE and pyramid dilated convolution.

Skin lesion segmentation is crucial for identifying and diagnosing skin diseases. Accurate segmentation aids in identifying and localizing diseases, m...

DRAMMA: a multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data.

BACKGROUND: Antibiotics are essential for medical procedures, food security, and public health. However, ill-advised usage leads to increased pathogen...

Classification of Mycena and Species Using Deep Learning Models: An Ecological and Taxonomic Approach.

Fungi play a critical role in ecosystems, contributing to biodiversity and providing economic and biotechnological value. In this study, we developed ...

Machine Learning-Assisted Prediction and Generation of Antimicrobial Peptides.

Antimicrobial peptides (AMPs) offer a highly potent alternative solution due to their broad-spectrum activity and minimum resistance development again...

Machine learning models accurately predict clades of proteocephalidean tapeworms (Onchoproteocephalidea) based on host and biogeographical data.

Proteocephalids are a cosmopolitan and diverse group of tapeworms (Cestoda) that have colonized vertebrate hosts in freshwater and terrestrial environ...