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dc.contributor.authorJimenez-Hernandez, M
dc.contributor.authorHughes, C
dc.contributor.authorBassan, P
dc.contributor.authorBall, F
dc.contributor.authorBrown, Michael D
dc.contributor.authorClarke, Noel W
dc.contributor.authorGardner, P
dc.date.accessioned2013-08-06T14:38:11Z
dc.date.available2013-08-06T14:38:11Z
dc.date.issued2013-07-21
dc.identifier.citationExploring the spectroscopic differences of Caki-2 cells progressing through the cell cycle while proliferating in vitro. 2013, 138 (14):3957-66 Analysten_GB
dc.identifier.issn1364-5528
dc.identifier.pmid23640135
dc.identifier.doi10.1039/c3an00507k
dc.identifier.urihttp://hdl.handle.net/10541/297461
dc.description.abstractFTIR micro-spectral images of Caki-2 cells cytospun onto calcium fluoride (CaF2) slides were used to build a computational model in order to discriminate between the biochemical events of the continuous cell cycle during proliferation. Multivariate analysis and machine learning techniques such as PCA, PLSR and SVMs were used to highlight the chemical differences among the cell cycle phases and also to point out the need for removing the distortion of the spectra due to the morphology of the cells. Results showed cell cycle dependant scattering profiles that enabled the training of a SVM in order to recognise, with a relative high accuracy, each cell cycle phase purely with the scattering curve removed from the FTIR data after being subject to the RMieS-EMSC algorithm.
dc.language.isoenen
dc.rightsArchived with thanks to The Analysten_GB
dc.titleExploring the spectroscopic differences of Caki-2 cells progressing through the cell cycle while proliferating in vitro.en
dc.typeArticleen
dc.contributor.departmentManchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. peter.gardner@manchester.ac.uk.en_GB
dc.identifier.journalThe Analysten_GB
html.description.abstractFTIR micro-spectral images of Caki-2 cells cytospun onto calcium fluoride (CaF2) slides were used to build a computational model in order to discriminate between the biochemical events of the continuous cell cycle during proliferation. Multivariate analysis and machine learning techniques such as PCA, PLSR and SVMs were used to highlight the chemical differences among the cell cycle phases and also to point out the need for removing the distortion of the spectra due to the morphology of the cells. Results showed cell cycle dependant scattering profiles that enabled the training of a SVM in order to recognise, with a relative high accuracy, each cell cycle phase purely with the scattering curve removed from the FTIR data after being subject to the RMieS-EMSC algorithm.


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