AIMC Topic: Escherichia coli

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Adapting nanopore sequencing basecalling models for modification detection via incremental learning and anomaly detection.

Nature communications
We leverage machine learning approaches to adapt nanopore sequencing basecallers for nucleotide modification detection. We first apply the incremental learning (IL) technique to improve the basecalling of modification-rich sequences, which are usuall...

Surface enhanced Raman spectroscopy and machine learning for identification of beta-lactam antibiotics resistance gene fragment in bacterial plasmid.

Analytica chimica acta
BACKGROUND: Antibiotic resistance stands as a critical medical concern, notably evident in commonly prescribed beta-lactam antibiotics. The imperative need for expeditious and precise early detection methods underscores their role in facilitating tim...

Unbiased identification of risk factors for invasive Escherichia coli disease using machine learning.

BMC infectious diseases
BACKGROUND: Invasive Escherichia coli disease (IED), also known as invasive extraintestinal pathogenic E. coli disease, is a leading cause of sepsis and bacteremia in older adults that can result in hospitalization and sometimes death and is frequent...

Discrimination of Common Strains in Urine by Liquid Chromatography-Ion Mobility-Tandem Mass Spectrometry and Machine Learning.

Journal of the American Society for Mass Spectrometry
Accurate identification of bacterial strains in clinical samples is essential to provide an appropriate antibiotherapy to the patient and reduce the prescription of broad-spectrum antimicrobials, leading to antibiotic resistance. In this study, we ut...

Differentially used codons among essential genes in bacteria identified by machine learning-based analysis.

Molecular genetics and genomics : MGG
Codon usage bias (CUB), the uneven usage of synonymous codons encoding the same amino acid, differs among genes within and across bacteria genomes. CUB is known to be influenced by gene expression and accordingly, CUB differs between the high-express...

Machine Learning Prediction of Small Molecule Accumulation in Enhanced with Descriptor Statistics.

Journal of chemical theory and computation
Antibiotic resistance, particularly among Gram-negative bacteria, poses a significant healthcare challenge due to their ability to evade antibiotic action through various mechanisms. In this study, we explore the prediction of small molecule accumula...

Identification of strains using MALDI-TOF MS combined with long short-term memory neural networks.

Aging
The current study aims to develop a new technique for the precise identification of strains, utilizing matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) combined with a long short-term memory (LSTM) neural n...

Enabling high-throughput enzyme discovery and engineering with a low-cost, robot-assisted pipeline.

Scientific reports
As genomic databases expand and artificial intelligence tools advance, there is a growing demand for efficient characterization of large numbers of proteins. To this end, here we describe a generalizable pipeline for high-throughput protein purificat...

Assessing the risk of E. coli contamination from manure application in Chinese farmland by integrating machine learning and Phydrus.

Environmental pollution (Barking, Essex : 1987)
This study aims to present a comprehensive study on the risks associated with the residual presence and transport of Escherichia coli (E. coli) in soil following the application of livestock manure in Chinese farmlands by integrating machine learning...