AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Bacteria

Showing 241 to 250 of 395 articles

Clear Filters

Same-day antimicrobial susceptibility test using acoustic-enhanced flow cytometry visualized with supervised machine learning.

Journal of medical microbiology
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 ...

Prediction of sgRNA on-target activity in bacteria by deep learning.

BMC bioinformatics
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
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
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
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
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
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 ...

Comparative efficacy of machine-learning models in prediction of reducing uncertainties in biosurfactant production.

Bioprocess and biosystems engineering
An accurate and reliable forecast of biosurfactant production with minimum error is useful in any bioprocess engineering. Bacterial isolate FKOD36 capable of producing biosurfactant was isolated in this study and pre-inoculums was prepared from the a...

Machine learning to predict microbial community functions: An analysis of dissolved organic carbon from litter decomposition.

PloS one
Microbial communities are ubiquitous and often influence macroscopic properties of the ecosystems they inhabit. However, deciphering the functional relationship between specific microbes and ecosystem properties is an ongoing challenge owing to the c...