4.6 Article

Antimicrobial resistance and machine learning: past, present, and future

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Medicine, General & Internal

Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis

Christopher J. L. Murray et al.

Summary: Antimicrobial resistance (AMR) poses a significant threat to global human health, and this study provides the most comprehensive estimates of AMR burden to date. By estimating deaths and disability-adjusted life-years (DALYs) associated with bacterial AMR in 204 countries and territories in 2019, the study highlights the impact of resistance and the leading pathogen-drug combinations contributing to it.

LANCET (2022)

Article Microbiology

A Practical Approach for Predicting Antimicrobial Phenotype Resistance in Staphylococcus aureus Through Machine Learning Analysis of Genome Data

Shuyi Wang et al.

Summary: With the reduction in sequencing price and acceleration of sequencing speed, it is increasingly important to directly link the genotype and phenotype of bacteria. In this study, machine learning algorithms were used to predict the minimum inhibitory concentrations of antimicrobial agents for Staphylococcus aureus, achieving high agreement rates and providing important information for clinical treatment.

FRONTIERS IN MICROBIOLOGY (2022)

Article Environmental Sciences

OdorTAM: Technology Acceptance Model for Biometric Authentication System Using Human Body Odor

Sameena Naaz et al.

Summary: This study proposed and validated a technology acceptance model (TAM) for body-odor-based biometric techniques. The results showed that body odor can be an alternative for authentication and can be combined with other techniques for improved security. The study contributes to understanding consumers' perception of biometric technologies, specifically odor detection.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2022)

Review Infectious Diseases

Deep Learning and Antibiotic Resistance

Stefan Lucian Popa et al.

Summary: This article discusses the causes and accelerating factors of antibiotic resistance, as well as the use of artificial intelligence techniques to expedite the identification and development of new antibiotics.

ANTIBIOTICS-BASEL (2022)

Article Infectious Diseases

Application of Decision-Tree-Based Machine Learning Algorithms for Prediction of Antimicrobial Resistance

Muhammad Yasir et al.

Summary: Timely and efficacious antibiotic treatment depends on precise and quick predictions of antimicrobial resistance. In this study, machine learning classifiers were trained using transcriptome data of multidrug-resistant Pseudomonas aeruginosa isolates to generate predictive models for meropenem, ciprofloxacin, and ceftazidime drugs. The findings show that transcriptomic markers can improve diagnostic performance for resistance profiling.

ANTIBIOTICS-BASEL (2022)

Article Polymer Science

Trend of Polymer Research Related to COVID-19 Pandemic: Bibliometric Analysis

Williams Chiari et al.

Summary: This report presents the trend of polymer research related to the COVID-19 pandemic through bibliometric analysis, focusing on personal protective equipment (PPE) and waste management. The field is primarily concentrated in the United States and China, with the Ministry of Education of China and the National Science Foundation of China being the most active institution and prominent funding source, respectively.

POLYMERS (2022)

Article Biology

AMR-meta: a k-mer and metafeature approach to classify antimicrobial resistance from high-throughput short-read metagenomics data

Simone Marini et al.

Summary: AMR-meta is a database-free and alignment-free approach for predicting antimicrobial resistance. It combines k-mers, algebraic matrix factorization, and regularized regression to capture gene diversity and improve accuracy. Compared to existing methods, AMR-meta achieves higher accuracy and faster runtime. Standardization of benchmarking data and protocols is needed to address differences in AMR ontologies and variability in classification outputs.

GIGASCIENCE (2022)

Review Biology

Accelerating antibiotic discovery through artificial intelligence

Marcelo C. R. Melo et al.

Summary: Antibiotics insert themselves into the ancient struggle of host-pathogen evolution, driving urgent interest in computational methods for candidate discovery. Advances in artificial intelligence have been applied to antibiotic discovery, emphasizing antimicrobial activity prediction, drug-likeness traits, resistance, and de novo molecular design. Best practices such as open science and reproducibility are crucial in accelerating preclinical research in the face of antimicrobial resistance crisis.

COMMUNICATIONS BIOLOGY (2021)

Review Biotechnology & Applied Microbiology

A review: antimicrobial resistance data mining models and prediction methods study for pathogenic bacteria

Xinxing Li et al.

Summary: Antimicrobials have been crucial for medical and social development, but the rapid spread and diversity of antibiotic-resistant pathogens pose a global challenge, making prediction and analysis difficult. This research reviews antimicrobial resistance data, assessment methods, and prediction methods based on machine learning and data mining techniques, offering comprehensive reference for further research in antimicrobial resistance.

JOURNAL OF ANTIBIOTICS (2021)

Article Biochemistry & Molecular Biology

A Deep Learning Approach to Antibiotic Discovery

Jonathan M. Stokes et al.

Article Oncology

A bibliometric analysis using VOSviewer of publications on COVID-19

Yuetian Yu et al.

ANNALS OF TRANSLATIONAL MEDICINE (2020)

Review Communication

Software tools for conducting bibliometric analysis in science: An up-to-date review

Jose A. Moral-Munoz et al.

PROFESIONAL DE LA INFORMACION (2020)

Review Microbiology

Drug combinations: a strategy to extend the life of antibiotics in the 21 st century

Mike Tyers et al.

NATURE REVIEWS MICROBIOLOGY (2019)

Article Biochemical Research Methods

Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data

Yang Yang et al.

BIOINFORMATICS (2018)

Review Cell & Tissue Engineering

Mortality after total hip replacement surgery A SYSTEMATIC REVIEW

J. R. Berstock et al.

BONE & JOINT RESEARCH (2014)

Article Computer Science, Interdisciplinary Applications

Software survey: VOSviewer, a computer program for bibliometric mapping

Nees Jan van Eck et al.

SCIENTOMETRICS (2010)