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Plasmids

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Aglow: A Fluorescence Assay and Machine Learning Model to Identify Inhibitors of Intracellular Infection.

ACS infectious diseases
is a genus of Gram-negative bacteria that has for centuries caused large-scale morbidity and mortality. In recent years, the resurgence of rickettsial diseases as a major cause of pyrexias of unknown origin, bioterrorism concerns, vector movement, a...

Neural-Like P Systems With Plasmids and Multiple Channels.

IEEE transactions on nanobioscience
Neural-like P systems with plasmids (NP P systems, in short) are a kind of distributed and parallel computing systems inspired by the activity that bacteria process DNA such as plasmids. An important biological fact is that one or more pili have exis...

PlasmidHunter: accurate and fast prediction of plasmid sequences using gene content profile and machine learning.

Briefings in bioinformatics
Plasmids are extrachromosomal DNA found in microorganisms. They often carry beneficial genes that help bacteria adapt to harsh conditions. Plasmids are also important tools in genetic engineering, gene therapy, and drug production. However, it can be...

Synergistic Polymer Blending Informs Efficient Terpolymer Design and Machine Learning Discerns Performance Trends for pDNA Delivery.

Bioconjugate chemistry
Cationic polymers offer an alternative to viral vectors in nucleic acid delivery. However, the development of polymer vehicles capable of high transfection efficiency and minimal toxicity has remained elusive, and continued exploration of the vast de...

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...

Data-driven learning of structure augments quantitative prediction of biological responses.

PLoS computational biology
Multi-factor screenings are commonly used in diverse applications in medicine and bioengineering, including optimizing combination drug treatments and microbiome engineering. Despite the advances in high-throughput technologies, large-scale experimen...

Identification of key drivers of antimicrobial resistance in using machine learning.

Canadian journal of microbiology
With antimicrobial resistance (AMR) rapidly evolving in pathogens, quick and accurate identification of genetic determinants of phenotypic resistance is essential for improving surveillance, stewardship, and clinical mitigation. Machine learning (ML)...

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...

Predicting the bacterial host range of plasmid genomes using the language model-based one-class support vector machine algorithm.

Microbial genomics
The prediction of the plasmid host range is crucial for investigating the dissemination of plasmids and the transfer of resistance and virulence genes mediated by plasmids. Several machine learning-based tools have been developed to predict plasmid h...

MOSTPLAS: a self-correction multi-label learning model for plasmid host range prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Plasmids play an essential role in horizontal gene transfer, aiding their host bacteria in acquiring beneficial traits like antibiotic and metal resistance. There exist some plasmids that can transfer, replicate, or persist in multiple or...