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Lookup NU author(s): Dr Chien-Yi ChangORCiD
This is the authors' accepted manuscript of an article that has been published in its final definitive form by Wiley - VCH Verlag GmbH & Co. KGaA, 2014.
For re-use rights please refer to the publisher's terms and conditions.
Infection by pathogenic bacteria on implanted and indwelling medical devices during surgery causes large morbidity and mortality worldwide. Attempts to ameliorate this important medical issue have included development of antimicrobial surfaces on materials, “no touch” surgical procedures, and development of materials with inherent low pathogen attachment. The search for new materials is increasingly being carried out by high throughput methods. Efficient methods for extracting knowledge from these large data sets are essential. Data from a large polymer microarray exposed to three clinical pathogens is used to derive robust and predictive machine-learning models of pathogen attachment. The models can predict pathogen attachment for the polymer library quantitatively. The models also successfully predict pathogen attachment for a second-generation library, and identify polymer surface chemistries that enhance or diminish pathogen attachment.
Author(s): Epa VC, Hook AL, Chang C, Yang J, Langer R, Anderson DG, Williams P, Davies MC, Alexander MR, Winkler DA
Publication type: Article
Publication status: Published
Journal: Advanced Functional Materials
Print publication date: 09/04/2014
Online publication date: 09/04/2014
Acceptance date: 04/12/2014
Date deposited: 06/08/2020
ISSN (print): 1616-301X
ISSN (electronic): 1616-3028
Publisher: Wiley - VCH Verlag GmbH & Co. KGaA
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