AIMC Topic: Bacteria

Clear Filters Showing 121 to 130 of 547 articles

Biofilm-mediated infections; novel therapeutic approaches and harnessing artificial intelligence for early detection and treatment of biofilm-associated infections.

Microbial pathogenesis
A biofilm is a group of bacteria that have self-produced a matrix and are grouped together in a dense population. By resisting the host's immune system's phagocytosis process and attacking with anti-microbial chemicals such as reactive oxygen and nit...

Revolutionizing biological digital twins: Integrating internet of bio-nano things, convolutional neural networks, and federated learning.

Computers in biology and medicine
Digital twins (DTs) are advancing biotechnology by providing digital models for drug discovery, digital health applications, and biological assets, including microorganisms. However, the hypothesis posits that implementing micro- and nanoscale DTs, e...

SERS-based approaches in the investigation of bacterial metabolism, antibiotic resistance, and species identification.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Surface-enhanced Raman scattering (SERS) is an inelastic scattering phenomenon that occurs when photons interact with substances, providing detailed molecular structure information. It exhibits various advantages including high sensitivity, specifici...

deep-Sep: a deep learning-based method for fast and accurate prediction of selenoprotein genes in bacteria.

mSystems
Selenoproteins are a special group of proteins with major roles in cellular antioxidant defense. They contain the 21st amino acid selenocysteine (Sec) in the active sites, which is encoded by an in-frame UGA codon. Compared to eukaryotes, identificat...

Enhancing Bacterial Phenotype Classification Through the Integration of Autogating and Automated Machine Learning in Flow Cytometric Analysis.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Although flow cytometry produces reliable results, the data processing from gating to fingerprinting is prone to subjective bias. Here, we integrated autogating with Automated Machine Learning in flow cytometry to enhance the classification of bacter...

Effect of training sample size, image resolution and epochs on filamentous and floc-forming bacteria classification using machine learning.

Journal of environmental management
Computer vision techniques can expedite the detection of bacterial growth in wastewater treatment plants and alleviate some of the shortcomings associated with traditional detection methods. In recent years, researchers capitalized on this potential ...

BacTermFinder: a comprehensive and general bacterial terminator finder using a CNN ensemble.

NAR genomics and bioinformatics
A terminator is a DNA region that ends the transcription process. Currently, multiple computational tools are available for predicting bacterial terminators. However, these methods are specialized for certain bacteria or terminator type (i.e. intrins...

DRAMMA: a multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data.

Microbiome
BACKGROUND: Antibiotics are essential for medical procedures, food security, and public health. However, ill-advised usage leads to increased pathogen resistance to antimicrobial substances, posing a threat of fatal infections and limiting the benefi...

New solutions for antibiotic discovery: Prioritizing microbial biosynthetic space using ecology and machine learning.

PLoS biology
With the explosive increase in genome sequence data, perhaps the major challenge in natural-product-based drug discovery is the identification of gene clusters most likely to specify new chemistry and bioactivities. We discuss the challenges and stat...