A review of using mathematical modeling to improve our understanding of bacteriophage, bacteria, and eukaryotic interactions

KM Styles, AT Brown, AP Sagona - Frontiers in Microbiology, 2021 - frontiersin.org
Phage therapy, the therapeutic usage of viruses to treat bacterial infections, has many
theoretical benefits in the 'post antibiotic era.'Nevertheless, there are currently no approved …

Artificial intelligence tools for the identification of antibiotic resistance genes

I Olatunji, DKR Bardaji, RR Miranda… - Frontiers in …, 2024 - frontiersin.org
The fight against bacterial antibiotic resistance must be given critical attention to avert the
current and emerging crisis of treating bacterial infections due to the inefficacy of clinically …

Better understanding and prediction of antiviral peptides through primary and secondary structure feature importance

AS Chowdhury, SM Reehl, K Kehn-Hall, B Bishop… - Scientific reports, 2020 - nature.com
The emergence of viral epidemics throughout the world is of concern due to the scarcity of
available effective antiviral therapeutics. The discovery of new antiviral therapies is …

Machine learning algorithm to characterize antimicrobial resistance associated with the International Space Station surface microbiome

P Madrigal, NK Singh, JM Wood, E Gaudioso… - Microbiome, 2022 - Springer
Background Antimicrobial resistance (AMR) has a detrimental impact on human health on
Earth and it is equally concerning in other environments such as space habitat due to …

Word2vec neural model-based technique to generate protein vectors for combating COVID-19: a machine learning approach

TA Adjuik, D Ananey-Obiri - International Journal of Information …, 2022 - Springer
The world was ambushed in 2019 by the COVID-19 virus which affected the health,
economy, and lifestyle of individuals worldwide. One way of combating such a public health …

PARGT: a software tool for predicting antimicrobial resistance in bacteria

AS Chowdhury, DR Call, SL Broschat - Scientific reports, 2020 - nature.com
With the ever-increasing availability of whole-genome sequences, machine-learning
approaches can be used as an alternative to traditional alignment-based methods for …

Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes

K Hu, F Meyer, ZL Deng, E Asgari, TH Kuo… - Briefings in …, 2024 - academic.oup.com
The advent of rapid whole-genome sequencing has created new opportunities for
computational prediction of antimicrobial resistance (AMR) phenotypes from genomic data …

Antimicrobial resistance and machine learning: challenges and opportunities

E Elyan, A Hussain, A Sheikh, AA Elmanama… - IEEE …, 2022 - ieeexplore.ieee.org
Antimicrobial Resistance (AMR) has been identified by the World Health Organisation
(WHO) as one of the top ten global health threats. Inappropriate use of antibiotics around the …

The role of artificial intelligence in the battle against antimicrobial-resistant bacteria

HJ Lau, CH Lim, SC Foo, HS Tan - Current genetics, 2021 - Springer
Antimicrobial resistance (AMR) in bacteria is a global health crisis due to the rapid
emergence of multidrug-resistant bacteria and the lengthy development of new …

Integrative genomics would strengthen AMR understanding through ONE health approach

CSC Liu, R Pandey - Heliyon, 2024 - cell.com
Emergence of drug-induced antimicrobial resistance (AMR) forms a crippling health and
economic crisis worldwide, causing high mortality from otherwise treatable diseases and …