AIMC Topic: Bacteria
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Exploiting open source 3D printer architecture for laboratory robotics to automate high-throughput time-lapse imaging for analytical microbiology.
PloS one
Nov 19, 2019
Growth in open-source hardware designs combined with the low-cost of high performance optoelectronic and robotics components has supported a resurgence of in-house custom lab equipment development. We describe a low cost (below $700), open-source, fu...
Prediction of essential genes in prokaryote based on artificial neural network.
Genes & genomics
Nov 17, 2019
BACKGROUND: Rapid identification of new essential genes is necessary to understand biological mechanisms and identify potential targets for antimicrobial drugs. Many computational methods have been proposed.
Same-day antimicrobial susceptibility test using acoustic-enhanced flow cytometry visualized with supervised machine learning.
Journal of medical microbiology
Oct 30, 2019
Antimicrobial susceptibility is slow to determine, taking several days to fully impact treatment. This proof-of-concept study assessed the feasibility of using machine-learning techniques for analysis of data produced by the flow cytometer-assisted ...
Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning.
Nature communications
Oct 30, 2019
Raman optical spectroscopy promises label-free bacterial detection, identification, and antibiotic susceptibility testing in a single step. However, achieving clinically relevant speeds and accuracies remains challenging due to weak Raman signal from...
Proteus mirabilis
Enterococcus
Streptococcus
Bacterial Typing Techniques
Candida
Bacterial Infections
Humans
Principal Component Analysis
Neural Networks, Computer
Pseudomonas aeruginosa
Anti-Bacterial Agents
Escherichia coli
Salmonella enterica
Logistic Models
Single-Cell Analysis
Staphylococcus aureus
Klebsiella
Spectrum Analysis, Raman
Bacteria
Methicillin-Resistant Staphylococcus aureus
Deep Learning
Support Vector Machine
Microbial Sensitivity Tests
Prediction of sgRNA on-target activity in bacteria by deep learning.
BMC bioinformatics
Oct 24, 2019
BACKGROUND: One of the main challenges for the CRISPR-Cas9 system is selecting optimal single-guide RNAs (sgRNAs). Recently, deep learning has enhanced sgRNA prediction in eukaryotes. However, the prokaryotic chromatin structure is different from euk...
A deep learning ensemble for function prediction of hypothetical proteins from pathogenic bacterial species.
Computational biology and chemistry
Oct 19, 2019
Protein function prediction is a crucial task in the post-genomics era due to their diverse irreplaceable roles in a biological system. Traditional methods involved cost-intensive and time-consuming molecular biology techniques but they proved to be ...
Fast and Accurate Bacterial Species Identification in Urine Specimens Using LC-MS/MS Mass Spectrometry and Machine Learning.
Molecular & cellular proteomics : MCP
Oct 4, 2019
Fast identification of microbial species in clinical samples is essential to provide an appropriate antibiotherapy to the patient and reduce the prescription of broad-spectrum antimicrobials leading to antibioresistances. MALDI-TOF-MS technology has ...
Use of high-content analysis and machine learning to characterize complex microbial samples via morphological analysis.
PloS one
Sep 23, 2019
High Content Analysis (HCA) has become a cornerstone of cellular analysis within the drug discovery industry. To expand the capabilities of HCA, we have applied the same analysis methods, validated in numerous mammalian cell models, to microbiology m...
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...
Identification of city specific important bacterial signature for the MetaSUB CAMDA challenge microbiome data.
Biology direct
Jul 24, 2019
BACKGROUND: Metagenomic data of whole genome sequences (WGS) from samples across several cities around the globe may unravel city specific signatures of microbes. Illumina MiSeq sequencing data was provided from 12 cities in 7 different countries as ...
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