AIMC Topic: Genome, Bacterial

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

Scientific reports Feb 14, 2022
Effective and timely antibiotic treatment depends on accurate and rapid in silico antimicrobial-resistant (AMR) predictions. Existing statistical rule-based Mycobacterium tuberculosis (MTB) drug resistance prediction methods using bacterial genomic s...
Phylogeny Deep Learning Microbial Sensitivity Tests Drug Resistance, Multiple, Bacterial Humans Phenotype Prognosis Whole Genome Sequencing Mycobacterium tuberculosis Data Accuracy Genome, Bacterial Cohort Studies Mutation Antitubercular Agents Tuberculosis, Multidrug-Resistant
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DeLUCS: Deep learning for unsupervised clustering of DNA sequences.

PloS one Jan 21, 2022
We present a novel Deep Learning method for the Unsupervised Clustering of DNA Sequences (DeLUCS) that does not require sequence alignment, sequence homology, or (taxonomic) identifiers. DeLUCS uses Frequency Chaos Game Representations (FCGR) of prim...
Animals Deep Learning Sequence Analysis, DNA Cluster Analysis Base Sequence Genome, Viral Genome, Mitochondrial Genome, Bacterial Humans Neural Networks, Computer
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Genome-Wide Mutation Scoring for Machine-Learning-Based Antimicrobial Resistance Prediction.

International journal of molecular sciences Dec 2, 2021
The prediction of antimicrobial resistance (AMR) based on genomic information can improve patient outcomes. Genetic mechanisms have been shown to explain AMR with accuracies in line with standard microbiology laboratory testing. To translate genetic ...
Machine Learning Drug Resistance, Bacterial Anti-Infective Agents Genomics Pseudomonas aeruginosa Genome, Bacterial Escherichia coli Mutation Whole Genome Sequencing
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GenTB: A user-friendly genome-based predictor for tuberculosis resistance powered by machine learning.

Genome medicine Aug 30, 2021
BACKGROUND: Multidrug-resistant Mycobacterium tuberculosis (Mtb) is a significant global public health threat. Genotypic resistance prediction from Mtb DNA sequences offers an alternative to laboratory-based drug-susceptibility testing. User-friendly...
Genomics Computational Biology Databases, Genetic Antitubercular Agents Reproducibility of Results Microbial Sensitivity Tests ROC Curve Tuberculosis, Multidrug-Resistant Workflow Genome, Bacterial Web Browser Humans Machine Learning Software Drug Resistance, Bacterial
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Application of the random forest algorithm to Streptococcus pyogenes response regulator allele variation: from machine learning to evolutionary models.

Scientific reports Jun 16, 2021
Group A Streptococcus (GAS) is a globally significant bacterial pathogen. The GAS genotyping gold standard characterises the nucleotide variation of emm, which encodes a surface-exposed protein that is recombinogenic and under immune-based selection ...
Bacterial Outer Membrane Proteins Antigens, Bacterial Genome, Bacterial Regulon Streptococcal Infections Genetic Variation Streptococcus pyogenes Genes, Bacterial Evolution, Molecular Bacterial Proteins Alleles Humans Machine Learning Algorithms Carrier Proteins
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Detecting operons in bacterial genomes via visual representation learning.

Scientific reports Jan 22, 2021
Contiguous genes in prokaryotes are often arranged into operons. Detecting operons plays a critical role in inferring gene functionality and regulatory networks. Human experts annotate operons by visually inspecting gene neighborhoods across pileups ...
Deep Learning Genomics Computational Biology Models, Genetic Escherichia coli Bacillus subtilis Gene Expression Regulation, Bacterial Genome, Bacterial Humans Algorithms Reproducibility of Results Operon
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Forest and Trees: Exploring Bacterial Virulence with Genome-wide Association Studies and Machine Learning.

Trends in microbiology Jan 14, 2021
The advent of inexpensive and rapid sequencing technologies has allowed bacterial whole-genome sequences to be generated at an unprecedented pace. This wealth of information has revealed an unanticipated degree of strain-to-strain genetic diversity w...
Genome-Wide Association Study Machine Learning Genomics Phylogeny Genotype Virulence Factors Bacteria Virulence Genetic Variation Genome, Bacterial
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Predicting antimicrobial resistance using conserved genes.

PLoS computational biology Oct 19, 2020
A growing number of studies are using machine learning models to accurately predict antimicrobial resistance (AMR) phenotypes from bacterial sequence data. Although these studies are showing promise, the models are typically trained using features de...
Conserved Sequence Algorithms Genomics Anti-Bacterial Agents Bacteria Machine Learning Drug Resistance, Bacterial Genome, Bacterial Phenotype
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Keeping up with the genomes: efficient learning of our increasing knowledge of the tree of life.

BMC bioinformatics Sep 21, 2020
BACKGROUND: It is a computational challenge for current metagenomic classifiers to keep up with the pace of training data generated from genome sequencing projects, such as the exponentially-growing NCBI RefSeq bacterial genome database. When new ref...
Bacteria Gastrointestinal Microbiome Humans Metagenomics Sequence Analysis, DNA Machine Learning Genome, Bacterial Bayes Theorem Algorithms Metagenome
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A Genome-Based Model to Predict the Virulence of Pseudomonas aeruginosa Isolates.

mBio Aug 25, 2020
Variation in the genome of , an important pathogen, can have dramatic impacts on the bacterium's ability to cause disease. We therefore asked whether it was possible to predict the virulence of isolates based on their genomic content. We applied a m...
Bacteremia Female Mice, Inbred BALB C Animals Machine Learning Mice Pseudomonas aeruginosa Virulence Genomics Proof of Concept Study Algorithms Pseudomonas Infections Phenotype Genome, Bacterial
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