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A mathematical framework for spectroscopy data analysis to characterize chemical-induced alterations in the SHE assay

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A mathematical framework for spectroscopy data analysis to characterize chemical-induced alterations in the SHE assay. / Trevisan, Julio; Angelov, Plamen P.; Carmichael, Paul L. et al.
In: Mutagenesis, Vol. 25, No. 6, 11.2010, p. 658-658.

Research output: Contribution to Journal/MagazineMeeting abstractpeer-review

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Trevisan J, Angelov PP, Carmichael PL, Scott AD, Martin FL. A mathematical framework for spectroscopy data analysis to characterize chemical-induced alterations in the SHE assay. Mutagenesis. 2010 Nov;25(6):658-658. doi: 10.1093/mutage/geq090

Author

Trevisan, Julio ; Angelov, Plamen P. ; Carmichael, Paul L. et al. / A mathematical framework for spectroscopy data analysis to characterize chemical-induced alterations in the SHE assay. In: Mutagenesis. 2010 ; Vol. 25, No. 6. pp. 658-658.

Bibtex

@article{42a20e6537d74583a6617c775e3c8bf4,
title = "A mathematical framework for spectroscopy data analysis to characterize chemical-induced alterations in the SHE assay",
abstract = "Acquisition of IR spectra often generates complex datasets that are not readily interpretable for the purposes of deriving biomarkers. From a computational perspective, this raises the question of what multi-step processing is required and, whether there is a well-defined sequence of steps that can be applied toobjectively shed insight into a biological question. To generate a dataset to investigate this, we set up an in vitro transformation assay (pH 6.7) using Syrian hamster embryo (SHE) cells (1). SHE cells were interrogated by ATR-FTIR spectroscopy.Derived mid-IR spectra (nspectra @14,000) were inputted into a computational framework designed for outlier removal, multivariate analysis and validation of the robustness of analysis, and biomarker identification. Biomarker identificationmethods were independently applied and compared to identify common discriminating chemical entities. Stable biomarkers of chemical-induced alterations or transformation were identified and confirmed. The analysis framework was implemented in the form of a user-friendly graphical user interface using a programming toolkit designed for research on computationalmethods. The database platform developed to store our dataset is scalable and can facilitate a data-sharing inter-laboratory process towards end-user applications for IR spectroscopy.",
author = "Julio Trevisan and Angelov, {Plamen P.} and Carmichael, {Paul L.} and Scott, {Andrew D.} and Martin, {Francis L.}",
year = "2010",
month = nov,
doi = "10.1093/mutage/geq090",
language = "English",
volume = "25",
pages = "658--658",
journal = "Mutagenesis",
issn = "0267-8357",
publisher = "OXFORD UNIV PRESS",
number = "6",

}

RIS

TY - JOUR

T1 - A mathematical framework for spectroscopy data analysis to characterize chemical-induced alterations in the SHE assay

AU - Trevisan, Julio

AU - Angelov, Plamen P.

AU - Carmichael, Paul L.

AU - Scott, Andrew D.

AU - Martin, Francis L.

PY - 2010/11

Y1 - 2010/11

N2 - Acquisition of IR spectra often generates complex datasets that are not readily interpretable for the purposes of deriving biomarkers. From a computational perspective, this raises the question of what multi-step processing is required and, whether there is a well-defined sequence of steps that can be applied toobjectively shed insight into a biological question. To generate a dataset to investigate this, we set up an in vitro transformation assay (pH 6.7) using Syrian hamster embryo (SHE) cells (1). SHE cells were interrogated by ATR-FTIR spectroscopy.Derived mid-IR spectra (nspectra @14,000) were inputted into a computational framework designed for outlier removal, multivariate analysis and validation of the robustness of analysis, and biomarker identification. Biomarker identificationmethods were independently applied and compared to identify common discriminating chemical entities. Stable biomarkers of chemical-induced alterations or transformation were identified and confirmed. The analysis framework was implemented in the form of a user-friendly graphical user interface using a programming toolkit designed for research on computationalmethods. The database platform developed to store our dataset is scalable and can facilitate a data-sharing inter-laboratory process towards end-user applications for IR spectroscopy.

AB - Acquisition of IR spectra often generates complex datasets that are not readily interpretable for the purposes of deriving biomarkers. From a computational perspective, this raises the question of what multi-step processing is required and, whether there is a well-defined sequence of steps that can be applied toobjectively shed insight into a biological question. To generate a dataset to investigate this, we set up an in vitro transformation assay (pH 6.7) using Syrian hamster embryo (SHE) cells (1). SHE cells were interrogated by ATR-FTIR spectroscopy.Derived mid-IR spectra (nspectra @14,000) were inputted into a computational framework designed for outlier removal, multivariate analysis and validation of the robustness of analysis, and biomarker identification. Biomarker identificationmethods were independently applied and compared to identify common discriminating chemical entities. Stable biomarkers of chemical-induced alterations or transformation were identified and confirmed. The analysis framework was implemented in the form of a user-friendly graphical user interface using a programming toolkit designed for research on computationalmethods. The database platform developed to store our dataset is scalable and can facilitate a data-sharing inter-laboratory process towards end-user applications for IR spectroscopy.

U2 - 10.1093/mutage/geq090

DO - 10.1093/mutage/geq090

M3 - Meeting abstract

VL - 25

SP - 658

EP - 658

JO - Mutagenesis

JF - Mutagenesis

SN - 0267-8357

IS - 6

ER -