NDLI: Cell Death Discrimination with Raman Spectroscopy and Support Vector Machines

Content Provider	Springer Nature Link
Author	Pyrgiotakis, Georgios Kundakcioglu, O. Erhun Finton, Kathryn Pardalos, Pas M. Powers, Kevin Moudgil, Brij M.
Copyright Year	2009
Abstract	In the present study, Raman spectroscopy is employed to assess the potential toxicity of chemical substances. Having several advantages compared to other traditional methods, Raman spectroscopy is an ideal solution for investigating cells in their natural environment. In the present work, we combine the power of spectral resolution of Raman with one of the most widely used machine learning techniques. Support vector machines (SVMs) are used in the context of classification on a well established database. The database is constructed on three different classes: healthy cells, Triton X-100 (necrotic death), and etoposide (apoptotic death). SVM classifiers successfully assess the potential effect of the test toxins (Triton X-100, etoposide). The cells that are exposed to heat (45 °C) are tested using the classification rules obtained. It is shown that the heat effect results in apoptotic death, which is in agreement with existing literature.
Starting Page	1464
Ending Page	1473
Page Count	10
File Format	PDF
ISSN	00906964
Journal	Annals of Biomedical Engineering
Volume Number	37
Issue Number	7
e-ISSN	15739686
Language	English
Publisher	Springer US
Publisher Date	2009-04-14
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Raman spectroscopy Support vector machines Death cell discrimination Toxic chemicals Cancer treatment Biochemistry Mechanics Biophysics/Biomedical Physics Biomedical Engineering Biomedicine general
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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