AIMC Topic: Escherichia coli

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Prediction of Antibiotic Susceptibility in E. coli Isolates Using Machine Learning.

Studies in health technology and informatics
Antimicrobial resistance (AMR) poses a significant global health threat, resulting in 4.96 million deaths in 2019, with projections reaching 10 million by 2050. This resistance, primarily due to the overuse of antibiotics, complicates the treatment o...

De novo synthetic antimicrobial peptide design with a recurrent neural network.

Protein science : a publication of the Protein Society
Antibiotic resistance is recognized as an imminent and growing global health threat. New antimicrobial drugs are urgently needed due to the decreasing effectiveness of conventional small-molecule antibiotics. Antimicrobial peptides (AMPs), a class of...

Transfer learning for cross-context prediction of protein expression from 5'UTR sequence.

Nucleic acids research
Model-guided DNA sequence design can accelerate the reprogramming of living cells. It allows us to engineer more complex biological systems by removing the need to physically assemble and test each potential design. While mechanistic models of gene e...

Species-specific design of artificial promoters by transfer-learning based generative deep-learning model.

Nucleic acids research
Native prokaryotic promoters share common sequence patterns, but are species dependent. For understudied species with limited data, it is challenging to predict the strength of existing promoters and generate novel promoters. Here, we developed Promo...

A machine learning-based approach for improving plasmid DNA production in Escherichia coli fed-batch fermentations.

Biotechnology journal
Artificial Intelligence (AI) technology is spearheading a new industrial revolution, which provides ample opportunities for the transformational development of traditional fermentation processes. During plasmid fermentation, traditional subjective pr...

Inference of gene regulatory networks based on directed graph convolutional networks.

Briefings in bioinformatics
Inferring gene regulatory network (GRN) is one of the important challenges in systems biology, and many outstanding computational methods have been proposed; however there remains some challenges especially in real datasets. In this study, we propose...

Optical sensing for real-time detection of food-borne pathogens in fresh produce using machine learning.

Science progress
Contaminated fresh produce remains a prominent catalyst for food-borne illnesses, prompting the need for swift and precise pathogen detection to mitigate health risks. This paper introduces an innovative strategy for identifying food-borne pathogens ...

A Machine Learning Approach for Predicting Essentiality of Metabolic Genes.

Methods in molecular biology (Clifton, N.J.)
The identification of essential genes is a key challenge in systems and synthetic biology, particularly for engineering metabolic pathways that convert feedstocks into valuable products. Assessment of gene essentiality at a genome scale requires larg...

Heterocyclic-Based Analogues against Sarcine-Ricin Loop RNA from : Molecular Docking Study and Machine Learning Classifiers.

Medicinal chemistry (Shariqah (United Arab Emirates))
BACKGROUND: Heterocyclic-based drugs have strong bioactivities, are active pharmacophores, and are used to design several antibacterial drugs. Due to the diverse biodynamic properties of well-known heterocyclic cores, such as quinoline, indole, and i...

Therapeutic effects of orally administration of viable and inactivated probiotic strains against murine urinary tract infection.

Journal of food and drug analysis
Urinary tract infections (UTIs) are highly prevalent bacterial infections that pose significant health risks. Specific probiotic strains have been recommended for UTI control and management of antibiotic resistance. Otherwise, para-probiotics, define...