dc.contributor.author |
Behling, Anna H |
|
dc.contributor.author |
Wilson, Brooke C |
|
dc.contributor.author |
Ho, Daniel |
|
dc.contributor.author |
Virta, Marko |
|
dc.contributor.author |
O'Sullivan, Justin M |
|
dc.contributor.author |
Vatanen, Tommi |
|
dc.coverage.spatial |
England |
|
dc.date.accessioned |
2023-05-18T04:38:18Z |
|
dc.date.available |
2023-05-18T04:38:18Z |
|
dc.date.issued |
2023-04 |
|
dc.identifier.citation |
(2023). Current Opinion in Microbiology, 74, 102305-. |
|
dc.identifier.issn |
1369-5274 |
|
dc.identifier.uri |
https://hdl.handle.net/2292/64062 |
|
dc.description.abstract |
The increasing prevalence of infections caused by antibiotic-resistant bacteria is a global healthcare crisis. Understanding the spread of resistance is predicated on the surveillance of antibiotic resistance genes within an environment. Bioinformatics and artificial intelligence (AI) methods applied to metagenomic sequencing data offer the capacity to detect known and infer yet-unknown resistance mechanisms, and predict future outbreaks of antibiotic-resistant infections. Machine learning methods, in particular, could revive the waning antibiotic discovery pipeline by helping to predict the molecular structure and function of antibiotic resistance compounds, and optimising their interactions with target proteins. Consequently, AI has the capacity to play a central role in guiding antibiotic stewardship and future clinical decision-making around antibiotic resistance. |
|
dc.format.medium |
Print-Electronic |
|
dc.language |
eng |
|
dc.publisher |
Elsevier BV |
|
dc.relation.ispartofseries |
Current opinion in microbiology |
|
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
|
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0/ |
|
dc.subject |
Antimicrobial Resistance |
|
dc.subject |
Biodefense |
|
dc.subject |
Vaccine Related |
|
dc.subject |
Emerging Infectious Diseases |
|
dc.subject |
Infectious Diseases |
|
dc.subject |
Prevention |
|
dc.subject |
5.1 Pharmaceuticals |
|
dc.subject |
5 Development of treatments and therapeutic interventions |
|
dc.subject |
Infection |
|
dc.subject |
3 Good Health and Well Being |
|
dc.subject |
0605 Microbiology |
|
dc.subject |
1108 Medical Microbiology |
|
dc.title |
Addressing antibiotic resistance: computational answers to a biological problem? |
|
dc.type |
Journal Article |
|
dc.identifier.doi |
10.1016/j.mib.2023.102305 |
|
pubs.begin-page |
102305 |
|
pubs.volume |
74 |
|
dc.date.updated |
2023-04-14T12:46:41Z |
|
dc.rights.holder |
Copyright: The authors |
en |
dc.identifier.pmid |
37031568 (pubmed) |
|
pubs.author-url |
https://www.ncbi.nlm.nih.gov/pubmed/37031568 |
|
pubs.publication-status |
Published |
|
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.subtype |
Review |
|
pubs.subtype |
Journal Article |
|
pubs.elements-id |
957504 |
|
pubs.org-id |
Liggins Institute |
|
dc.identifier.eissn |
1879-0364 |
|
dc.identifier.pii |
S1369-5274(23)00042-5 |
|
pubs.number |
102305 |
|
pubs.record-created-at-source-date |
2023-04-15 |
|
pubs.online-publication-date |
2023-04-07 |
|