4.6 Review

Machine Learning of the Whole Genome Sequence of Mycobacterium tuberculosis: A Scoping PRISMA-Based Review

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 Biotechnology & Applied Microbiology

A modified decision tree approach to improve the prediction and mutation discovery for drug resistance in Mycobacterium tuberculosis

Wouter Deelder et al.

Summary: This article introduces a machine learning approach customized to the tuberculosis (TB) setting. It predicts drug resistance in Mycobacterium tuberculosis by evaluating genomic variants from multiple studies and identifies new resistance-encoding genetic mutations. The approach shows similar accuracy to existing tools for known resistance mutations and improved sensitivity for less understood drugs.

BMC GENOMICS (2022)

Article Multidisciplinary Sciences

Accurate and rapid prediction of tuberculosis drug resistance from genome sequence data using traditional machine learning algorithms and CNN

Xingyan Kuang et al.

Summary: This study developed a deep learning architecture (1D CNN) for predicting drug resistance in Mycobacterium tuberculosis (MTB), with higher accuracy and stability than existing rule-based prediction tools. Additionally, the selected features were mostly related to drug resistance and have been identified by the World Health Organization (WHO) as being associated with drug resistance.

SCIENTIFIC REPORTS (2022)

Article Biochemical Research Methods

Drug resistance prediction and resistance genes identification in Mycobacterium tuberculosis based on a hierarchical attentive neural network utilizing genome-wide variants

Zhonghua Jiang et al.

Summary: Prediction of antimicrobial resistance using whole-genome sequencing data has been improved by a novel hierarchical attentive neural network model, which accurately identifies drug resistance-related genes and variants.

BRIEFINGS IN BIOINFORMATICS (2022)

Article Multidisciplinary Sciences

A convolutional neural network highlights mutations relevant to antimicrobial resistance in Mycobacterium tuberculosis

Anna G. Green et al.

Summary: Pathogen whole genome sequencing, coupled with statistical and machine learning models, provides a promising solution for multi-drug resistance diagnosis. The authors have developed two deep convolutional neural networks that accurately predict antibiotic resistance phenotypes of M. tuberculosis isolates, with high sensitivity and specificity.

NATURE COMMUNICATIONS (2022)

Article Biochemical Research Methods

INGOT-DR: an interpretable classifier for predicting drug resistance in M. tuberculosis

Hooman Zabeti et al.

Summary: Predicting drug resistance and identifying mechanisms in bacteria like Mycobacterium tuberculosis is challenging. Traditional methods lack accuracy and flexibility, while machine learning methods lack interpretability. This paper introduces a novel technique inspired by group testing and Boolean compressed sensing, which provides highly accurate and interpretable results that can be customized for different evaluation metrics.

ALGORITHMS FOR MOLECULAR BIOLOGY (2021)

Article Genetics & Heredity

GenTB: A user-friendly genome-based predictor for tuberculosis resistance powered by machine learning

Matthias Groschel et al.

Summary: GenTB is a free and open online tool designed to rapidly and accurately predict resistance to anti-tuberculosis drugs.

GENOME MEDICINE (2021)

Article Microbiology

Predicting Antimicrobial Resistance Using Partial Genome Alignments

D. Aytan-Aktug et al.

Summary: The study developed machine learning classifiers to predict multiple bacterial antimicrobial resistance (AMR) phenotypes, revealing predictive value even in very small chromosomal regions, potentially related to resistance, virulence, transport, and survival under stress conditions. In addition to known AMR genes, many genes unrelated to resistance were identified as well.

MSYSTEMS (2021)

Article Computer Science, Information Systems

An explainable machine learning platform for pyrazinamide resistance prediction and genetic feature identification of Mycobacterium tuberculosis

Andrew Zhang et al.

Summary: The study aims to use machine learning technology to rapidly diagnose tuberculosis resistance and identify genetic features causing resistance. The results show that the machine learning platform can accurately predict resistance and identify relevant genes and mutations, potentially aiding in timely diagnosis and effective control of drug-resistant tuberculosis.

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2021)

Article Multidisciplinary Sciences

A biochemically-interpretable machine learning classifier for microbial GWAS

Erol S. Kavvas et al.

NATURE COMMUNICATIONS (2020)

Article Biochemical Research Methods

Genome-Wide Analysis of MDR and XDR Tuberculosis from Belarus: Machine-Learning Approach

Roman Sergeevich Sergeev et al.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2019)

Article Biochemical Research Methods

DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis

Yang Yang et al.

BIOINFORMATICS (2019)

Article Biotechnology & Applied Microbiology

Capreomycin resistance prediction in two species of Mycobacterium using a stacked ensemble method

A. S. Chowdhury et al.

JOURNAL OF APPLIED MICROBIOLOGY (2019)

Article Biochemical Research Methods

Application of machine learning techniques to tuberculosis drug resistance analysis

Samaneh Kouchaki et al.

BIOINFORMATICS (2019)

Review Microbiology

Genome-Based Prediction of Bacterial Antibiotic Resistance

Michelle Su et al.

JOURNAL OF CLINICAL MICROBIOLOGY (2019)