AIMC Topic: Genome, Bacterial

Clear Filters Showing 41 to 50 of 92 articles

Synthesis Success Calculator: Predicting the Rapid Synthesis of DNA Fragments with Machine Learning.

ACS synthetic biology Jun 30, 2020
The synthesis and assembly of long DNA fragments has greatly accelerated synthetic biology and biotechnology research. However, long turnaround times or synthesis failures create unpredictable bottlenecks in the design-build-test-learn cycle. We deve...
Machine Learning Software DNA Fragmentation Plasmids Nucleic Acid Conformation Genome, Bacterial Escherichia coli Databases, Genetic DNA
View on PubMed DOI

A biochemically-interpretable machine learning classifier for microbial GWAS.

Nature communications May 22, 2020
Current machine learning classifiers have successfully been applied to whole-genome sequencing data to identify genetic determinants of antimicrobial resistance (AMR), but they lack causal interpretation. Here we present a metabolic model-based machi...
Genome, Microbial Isoniazid Genome, Bacterial Aminosalicylic Acid Anti-Bacterial Agents Drug Resistance, Bacterial Reproducibility of Results Genome-Wide Association Study Mycobacterium tuberculosis Pyrazinamide Machine Learning
View on PubMed DOI

Photosynthetic protein classification using genome neighborhood-based machine learning feature.

Scientific reports Apr 28, 2020
Identification of novel photosynthetic proteins is important for understanding and improving photosynthetic efficiency. Synergistically, genome neighborhood can provide additional useful information to identify photosynthetic proteins. We, therefore,...
Bacteria Genome, Bacterial Models, Genetic Photosynthesis Support Vector Machine Bacterial Proteins
View on PubMed DOI

Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics.

EMBO molecular medicine Feb 12, 2020
Limited therapy options due to antibiotic resistance underscore the need for optimization of current diagnostics. In some bacterial species, antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we seq...
Machine Learning Anti-Bacterial Agents Drug Resistance, Bacterial Genome, Bacterial Pathology, Molecular Transcriptome Microbial Sensitivity Tests Pseudomonas aeruginosa
View on PubMed DOI

VAMPr: VAriant Mapping and Prediction of antibiotic resistance via explainable features and machine learning.

PLoS computational biology Jan 13, 2020
Antimicrobial resistance (AMR) is an increasing threat to public health. Current methods of determining AMR rely on inefficient phenotypic approaches, and there remains incomplete understanding of AMR mechanisms for many pathogen-antimicrobial combin...
Whole Genome Sequencing Genome, Bacterial Chromosome Mapping Algorithms Anti-Bacterial Agents Bacteria DNA, Bacterial Machine Learning Models, Statistical Software Drug Resistance, Microbial
View on PubMed DOI

A machine learning-based service for estimating quality of genomes using PATRIC.

BMC bioinformatics Oct 3, 2019
BACKGROUND: Recent advances in high-volume sequencing technology and mining of genomes from metagenomic samples call for rapid and reliable genome quality evaluation. The current release of the PATRIC database contains over 220,000 genomes, and curre...
Genome, Archaeal Databases, Genetic Metagenomics Genome, Bacterial Machine Learning Software
View on PubMed DOI

Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data.

PLoS computational biology Sep 3, 2019
Prediction of antibiotic resistance phenotypes from whole genome sequencing data by machine learning methods has been proposed as a promising platform for the development of sequence-based diagnostics. However, there has been no systematic evaluation...
Humans Computational Biology Databases, Genetic Bacterial Infections Reproducibility of Results Machine Learning Whole Genome Sequencing Algorithms Microbial Sensitivity Tests Genome, Bacterial Anti-Bacterial Agents Bacteria
View on PubMed DOI

Identification of prokaryotic promoters and their strength by integrating heterogeneous features.

Genomics Aug 19, 2019
The promoter is a regulatory DNA region and important for gene transcriptional regulation. It is located near the transcription start site (TSS) upstream of the corresponding gene. In the post-genomics era, the availability of data makes it possible ...
Deep Learning Sequence Analysis, DNA Promoter Regions, Genetic Genome, Bacterial Genome, Archaeal Software
View on PubMed DOI

Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data.

Nature communications Jun 4, 2019
DNA base modifications, such as C5-methylcytosine (5mC) and N6-methyldeoxyadenosine (6mA), are important types of epigenetic regulations. Short-read bisulfite sequencing and long-read PacBio sequencing have inherent limitations to detect DNA modifica...
Chlamydomonas reinhardtii Nanopores Genome, Bacterial Databases, Nucleic Acid Genome, Plant Escherichia coli DNA Methylation Genome, Human Humans Neural Networks, Computer Epigenesis, Genetic
View on PubMed DOI

Beyond multidrug resistance: Leveraging rare variants with machine and statistical learning models in Mycobacterium tuberculosis resistance prediction.

EBioMedicine Apr 29, 2019
BACKGROUND: The diagnosis of multidrug resistant and extensively drug resistant tuberculosis is a global health priority. Whole genome sequencing of clinical Mycobacterium tuberculosis isolates promises to circumvent the long wait times and limited s...
Mycobacterium tuberculosis Genome, Bacterial Extensively Drug-Resistant Tuberculosis Antitubercular Agents Tuberculosis, Multidrug-Resistant Genomics Genetic Variation Computational Biology Reproducibility of Results Cluster Analysis Humans Databases, Genetic Microbial Sensitivity Tests Machine Learning Models, Statistical ROC Curve Prognosis Evolution, Molecular
View on PubMed DOI
Popular Topics
Recent Journals