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International Conference on Learning and Intelligent Optimization

LION 2014: Learning and Intelligent Optimization pp 306–323Cite as

Raman Spectroscopy Using a Multiclass Extension of Fisher-Based Feature Selection Support Vector Machines (FFS-SVM) for Characterizing In-Vitro Apoptotic Cell Death Induced by Paclitaxel

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8426))

Abstract

Raman microspectroscopy combined with advanced data mining methods are used to demonstrate proof-of-concept for the development of a non-invasive, real-time in vitro assay platform for the classification and characterization of anti-cancer agents. Breast cancer cells were investigated over a 48 h time course of treatment with Paclitaxel. Raman spectroscopic analysis is used with a multiclass One-versus-One Support Vector Machines classification algorithm to classify cell death over a 48 h period. The Fisher-based Feature Selection method provides discriminative features descriptive of the apoptotic process during time-course. Spectral datasets collected at each of the time-points during a separate 48 h 3-point time course study are used as the testing datasets. The features, or spectral peaks, output directly as wavenumbers are correlated to corresponding biochemical species for each time point yielding an analysis of the biochemical compositional changes. Conventional assay methods are employed to validate and confirm results of the Raman spectroscopic analysis.

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Acknowledgements

The Authors would like to acknowledge the University of Florida Research Foundation and the UF Seed Opportunity Fund for providing funding for this work. The Authors would also like to thank the Particle Engineering Research Center and the Center for Applied Optimization at the University of Florida, Gainesville, Florida for allowing this work to be carried out in these laboratories respectively.

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Florida Institute of Technology, Melbourne, FL, USA

    Michael Fenn

  2. Department of High Performance Computing and Networking, National Research Council, Naples, Italy

    Mario Guarracino

  3. J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA

    Jiaxing Pi & Panos M. Pardalos

  4. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  5. Center for Applied Optimization, University of Florida, Gainesville, USA

    Michael Fenn, Jiaxing Pi & Panos M. Pardalos

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  1. Michael Fenn
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  2. Mario Guarracino
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  3. Jiaxing Pi
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  4. Panos M. Pardalos
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Corresponding author

Correspondence to Michael Fenn .

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Editors and Affiliations

  1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  2. AT&T Labs Research, Middletown, New Jersey, USA

    Mauricio G.C. Resende

  3. University of Florida, Gainesville, Florida, USA

    Chrysafis Vogiatzis

  4. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Jose L. Walteros

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Fenn, M., Guarracino, M., Pi, J., Pardalos, P.M. (2014). Raman Spectroscopy Using a Multiclass Extension of Fisher-Based Feature Selection Support Vector Machines (FFS-SVM) for Characterizing In-Vitro Apoptotic Cell Death Induced by Paclitaxel. In: Pardalos, P., Resende, M., Vogiatzis, C., Walteros, J. (eds) Learning and Intelligent Optimization. LION 2014. Lecture Notes in Computer Science(), vol 8426. Springer, Cham. https://doi.org/10.1007/978-3-319-09584-4_27

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  • DOI: https://doi.org/10.1007/978-3-319-09584-4_27

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