Abstract
Discrimination of different cell types is very important in many medical and biological applications. Existing methodologies are based on cost inefficient technologies or tedious one-by-one empirical examination of the cells. Recently, Raman spectroscopy, a inexpensive and efficient method, has been employed for cell discrimination. Nevertheless, the traditional protocols for analyzing Raman spectra require preprocessing and peak fitting analysis which does not allow simultaneous examination of many spectra. In this paper we examine the applicability of supervised learning algorithms in the cell differentiation problem. Five different methods are presented and tested on two different datasets. Computational results show that machine learning algorithms can be employed in order to automate cell discrimination tasks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Powers, K., Brown, S., Krishna, V., Wasdo, S., Moudgil, B., Roberts, S.: Research strategies for safety evaluation of nanomaterials. Part VI. Characterization of nanoscale particles for toxicological evaluation. Toxicological Sciences 90, 296–303 (2006)
Bhowmick, T., Pyrgiotakis, G., Finton, K., Suresh, A., Kane, S., Moudgil, B., Bellare, J.: A study of the effect of JB particles on Saccharomyces cerevisiae (yeast) cells by Raman spectroscopy. Journal of Raman Spectroscopy 39, 1859–1868 (2008)
Pardalos, P., Boginski, V., Vazacopoulos, A.: Data Mining in Biomedicine. Springer, Heidelberg (2007)
Seref, O., Kundakcioglu, O., Pardalos, P.: Data Mining, Systems Analysis, and Optimization in Biomedicine. In: AIP Conference Proceedings (2007)
Pyrgiotakis, G., Kundakcioglu, O.E., Finton, K., Pardalos, P.M., Powers, K., Moudgil, B.M.: Cell death discrimination with Raman spectroscopy and Support Vector Machines. Annals of Biomedical Engineering 37, 1464–1473 (2009)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Mangasarian, O.L., Wild, E.W.: Multisurface proximal Support Vector Machine classification via generalized eigenvalues. IEEE Trans. Pattern Anal. Mach. Intell. 28, 69–74 (2006)
Parlett, B.N.: The Symmetric Eigenvalue Problem (Classics in Applied Mathematics). SIAM, Philadelphia (1987)
Fisher, R.: The use of multiple measurements in taxonomic problems. Annals of Eugenics 7, 179–188 (1936)
Yan, R.: MATLAB Arsenal-A Matlab Package for Classification Algorithms. Informedia, School of Computer Science, Carnegie Mellon University (2006)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Guarracino, M.R., Cifarelli, C., Seref, O., Pardalos, P.M.: A classification method based on generalized eigenvalue problems. Optimization Methods and Software 22, 73–81 (2007)
Guarracino, M., Cifarelli, C., Seref, O., Pardalos, P.: A parallel classification method for genomic and proteomic problems. In: 20th International Conference on Advanced Information Networking and Applications, AINA 2006, vol. 2, pp. 588–592 (2006)
Della Pietra, S., Della Pietra, V., Lafferty, J., Technol, R., Brook, S.: Inducing features of random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 380–393 (1997)
Notingher, I., Green, C., Dyer, C., Perkins, E., Hopkins, N., Lindsay, C., Hench, L.L.: Discrimination between ricin and sulphur mustard toxicity in vitro using Raman spectroscopy. Journal of The Royal Society Interface 1, 79–90 (2004)
Owen, C.A., Selvakumaran, J., Notingher, I., Jell, G., Hench, L.L., Stevens, M.M.: In vitro toxicology evaluation of pharmaceuticals using Raman micro-spectroscopy. J. Cell. Biochem. 99, 178–186 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xanthopoulos, P., De Asmundis, R., Guarracino, M.R., Pyrgiotakis, G., Pardalos, P.M. (2011). Supervised Classification Methods for Mining Cell Differences as Depicted by Raman Spectroscopy. In: Rizzo, R., Lisboa, P.J.G. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2010. Lecture Notes in Computer Science(), vol 6685. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21946-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-21946-7_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21945-0
Online ISBN: 978-3-642-21946-7
eBook Packages: Computer ScienceComputer Science (R0)