Raman spectroscopy-based label-free cell identification using wavelet transform and support vector machine-Reference-Cited by-同舟云学术

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Raman spectroscopy-based label-free cell identification using wavelet transform and support vector machine

Published:2016 Issue:55 Volume:6 Page:50027-50033
ISSN:2046-2069
Container-title:RSC Advances
language:en
Short-container-title:RSC Adv.

Author:

Bakhtiaridoost S.¹²³⁴,Habibiyan H.¹²³⁴,Muhammadnejad S.⁵⁶³⁴,Haddadi M.⁷⁸⁶³⁴,Ghafoorifard H.⁹²³⁴,Arabalibeik H.¹⁰⁶³⁴,Amanpour S.⁷⁸⁶³⁴

Affiliation:

1. Photonics Engineering Group

2. Amirkabir University of Technology

3. Tehran

4. Iran

5. Research Center for Molecular and Cellular Imaging

6. Tehran University of Medical Sciences

7. Cancer Models Research Center

8. Cancer Institute

9. Department of Electrical Engineering

10. Research Center for Biomedical Technology and Robotics (RCBTR)

Abstract

Wavelet transform and SVM applied to Raman spectra makes a powerful and accurate tool for identification of rare cells such as CTCs.

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemical Engineering,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2016/RA/C6RA01004K

Reference45 articles.

1. Review: Biological relevance of disseminated tumor cells in cancer patients

2. Circulating tumor cells: approaches to isolation and characterization

3. Technologies for label-free separation of circulating tumor cells: from historical foundations to recent developments

4. Molecular characterization of circulating tumor cells in large quantities of contaminating leukocytes by a multiplex real-time PCR

5. Negative enrichment by immunomagnetic nanobeads for unbiased characterization of circulating tumor cells from peripheral blood of cancer patients

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3. The statistical fusion identification of dairy products based on extracted Raman spectroscopy;RSC Advances;2020

4. Raman profile alterations of irradiated human nasopharyngeal cancer cells detected with laser tweezer Raman spectroscopy;RSC Advances;2020

5. Accuracy improvement of quantitative LIBS analysis using wavelet threshold de-noising;Journal of Analytical Atomic Spectrometry;2017

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