A linear, binary classifier to predict bacterial biofilm formation on polyacrylates
dc.contributor.author | Williams, Phil | |
dc.contributor.author | Alexander, Morgan R. | |
dc.contributor.other | Laughton, Charles | |
dc.date.accessioned | 2022-09-08T07:55:19Z | |
dc.date.available | 2022-09-08T07:55:19Z | |
dc.date.issued | 2022-09-08 | |
dc.identifier.uri | https://rdmc.nottingham.ac.uk/handle/internal/9556 | |
dc.description.abstract | Datafiles, in Microsoft Excel format, associated with the manuscript "A linear, binary classifier to predict bacterial biofilm formation on polyacrylates" These files contain ToF-SIMS ion peaks and the bacterial attachment data. | en_UK |
dc.language.iso | en | en_UK |
dc.publisher | The University of Nottingham | en_UK |
dc.rights | CC-BY | * |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
dc.subject.lcsh | Time-of-flight mass spectrometry | en_UK |
dc.subject.lcsh | Biofilms | en_UK |
dc.subject.lcsh | Biomedical materials | en_UK |
dc.subject.lcsh | Polymers | en_UK |
dc.title | A linear, binary classifier to predict bacterial biofilm formation on polyacrylates | en_UK |
dc.identifier.doi | http://doi.org/10.17639/nott.7235 | |
dc.subject.free | ToF-SIMS, bacterial attachment, machine learning, molecular descriptors | en_UK |
dc.subject.jacs | Subjects Allied to Medicine | en_UK |
dc.subject.jacs | Computer Sciences::Artificial intelligence::Machine learning, Automated reasoning | en_UK |
dc.subject.jacs | Biological Sciences::Microbiology::Bacteriology | en_UK |
dc.subject.lc | Q Science::QR Microbiology::QR 75 Bacteria. Cyanobacteria | en_UK |
uon.division | University of Nottingham, UK Campus::Faculty of Science::School of Pharmacy | en_UK |
uon.funder.controlled | Engineering & Physical Sciences Research Council | en_UK |
uon.datatype | ToF-SIMS ion peaks and bacterial attachment data | en_UK |
uon.grant | EP/N006615/1 | en_UK |
uon.collectionmethod | ToF-SIMS and fluorescence microscopy | en_UK |
uon.preservation.rarelyaccessed | true |
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