Skip to main content
SpringerLink
Log in

Support Vector Machines in Biomedical and Biometrical Applications

  • Chapter
Book cover Emerging Paradigms in Machine Learning

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 13))

Abstract

In the chapter, a background review material concerning applications of the kernel methods in computational biology and biometry is illustrated by the case studies concerning the proteomic spectra analysis to find diagnostic biomarkers and performing case-control discrimination as well as the face recognition problem, which is situated among the most investigated biometric methods. These case studies, representing the state-of-the-art in applications of the support vector machines (SVM) in biomedical and biometrical applications, are the examples of a research work conducted by computer scientists, bioinformaticians, and biostatisticians from the Faculty of Automatic Control, Electronics and Computer Science at Silesian University of Technology in a collaboration with clinicists from the Institute of Oncology in Gliwice, Poland.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aebersold, R., Mann, M.: Mass spectrometry-based proteomics. Nature (422) (2003)

    Google Scholar 

  2. Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face recognition by independent component analysis. IEEE Transactions on Neural Networks 13, 1450–1464 (2002)

    Article  Google Scholar 

  3. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 711–720 (1997)

    Article  Google Scholar 

  4. Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, pp. 144–152 (1992)

    Google Scholar 

  5. Box, G.E.P., Cox, D.R.: An analysis of transformations. JSTOR 62(2), 211–252 (1964)

    MathSciNet  Google Scholar 

  6. Cao, L.J., Tay, F.E.H.: Support vector machine with adaptive parameters in financial time series forecasting. IEEE Transactions on Neural Networks 14(6), 1506–1518 (2003)

    Article  Google Scholar 

  7. Cauwenberghs, G., Poggio, T.: Incremental and decremental support vector machine learning. In: Advances in Neural Information Processing Systems 13: Proceedings of the 2000 Conference, p. 409. The MIT Press (2001)

    Google Scholar 

  8. Chapelle, O.: Training a support vector machine in the primal. Neural Computation 19(5), 1155–1178 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  9. Chen, X., Yang, J., Liang, J., Ye, Q.: Smooth twin support vector regression. Neural Computing and Applications, 1–9 (2010), http://dx.doi.org/10.1007/s00521-010-0454-9

  10. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press (2000)

    Google Scholar 

  11. Drucker, H., Burges, C.J.C., Kaufman, L., Smola, A., Vapnik, V.: Support vector regression machines. In: Mozer, M.C., Jordan, M.I., Petsche, T. (eds.) Advances in Neural Information Processing Systems 9, pp. 155–161. MIT Press, Cambridge (1997)

    Google Scholar 

  12. Fernández, R.: Predicting time series with a local support vector regression machine. In: Proceedings of the ECCAI Advanced Course on Artificial Intelligence, ACAI 1999 (1999)

    Google Scholar 

  13. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Co. (1989)

    Google Scholar 

  14. Gong, S., McKenna, S.J., Psarrou, A.: Dynamic Vision From Images to Face Recognition. Imperial College Press (1999)

    Google Scholar 

  15. Grother, P., Micheals, R., Phillips, P.J.: Face recognition vendor test 2002 performance metrics. In: Proceedings of the Fourth International Conference on Audio-Visual Based Person Authentication (2003)

    Google Scholar 

  16. Hao, P.-Y.: New support vector algorithms with parametric insensitive/margin model. Neural Networks 23, 60–73 (2010)

    Article  Google Scholar 

  17. Hastie, T., Tibshrani, R., Friedman, J.: Clinical Proteomics: From Diagnosis to Therapy. Springer (2001)

    Google Scholar 

  18. Hilario, M., Kalousis, A., Pellegrini, C., Müller, M.: Processing and classification of protein mass spectra. Bioinformatics 25(3), 409–449 (2006)

    Google Scholar 

  19. Hochreiter, S., Obermayer, K.: Gene selection for microarray data. In: Scholkopf, B., Tsuda, K., Vert, J.P. (eds.) Kernel Methods in Computational Biology, pp. 319–355. MIT Press (2004)

    Google Scholar 

  20. Huang, J., Blanz, V., Heisele, B.: Face Recognition using Component-based SVM Classification and Morphable Models. In: Lee, S.-W., Verri, A. (eds.) SVM 2002. LNCS, vol. 2388, pp. 334–341. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  21. Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust face detection using the hausdorff distance. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 90–95. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  22. Joachims, T.: Text categorization with support vector machines: Learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  23. Joachims, T.: Training linear svms in linear time. In: Eliassi-Rad, T., Ungar, L.H., Craven, M., Gunopulos, D. (eds.) KDD, pp. 217–226. ACM (2006)

    Google Scholar 

  24. Kanji, G.K.: 100 statistical tests, 3rd edn. SAGE Publications Ltd (2006)

    Google Scholar 

  25. Karpievitch, Y.V., Hill, E.G., Smolka, A.J., Morris, J.S., Coombes, K.R., Baggerly, K.A., Almeida, J.S.: Prepms: Tof ms data graphical preprocessing tool. Bioinformatics 23(2), 264–265 (2007)

    Article  Google Scholar 

  26. Kawulok, M., Szymanek, J.: Algorithm for precise frontal face detection. Studia Informatica 30, 341–354 (2009)

    Google Scholar 

  27. Kawulok, M., Wu, J., Hancock, E.R.: Supervised relevance maps for increasing the distinctiveness of facial images. Pattern Recognition 44(4), 929–939 (2011)

    Article  Google Scholar 

  28. Kin, T., Kato, T., Tsuda, K.: Protein classification via kernel matrix completion. In: Scholkopf, B., Tsuda, K., Vert, J.P. (eds.) Kernel Methods in Computational Biology, pp. 261–274. MIT Press (2004)

    Google Scholar 

  29. Krishnapuram, B., Carin, L., Hartemink, A.: Gene expression analysis: Joint feature selection and classifier design. In: Scholkopf, B., Tsuda, K., Vert, J.P. (eds.) Kernel Methods in Computational Biology, pp. 299–317. MIT Press (2004)

    Google Scholar 

  30. Lee, Y.J., Huang, S.Y.: Reduced support vector machines: A statistical theory. IEEE Transactions on Neural Networks 18(1), 1–13 (2006)

    Article  Google Scholar 

  31. Leski, J.: On support vector regression machines with linguistic interpretation of the kernel matrix. Fuzzy Sets and Systems 157, 1092–1113 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  32. Lilliefors, H.L.: On the kolmogorov–smirnov test for normality with mean and variance unknown. JASA 62, 399–402 (1967)

    Google Scholar 

  33. Liu, Q., Krishnapuram, B., Pratapa, P., Liao, X., Hartemink, E., Carin, L.: Identification of differentially expressed proteins using maldi-tof mass spectra. In: Asilomar Conference: Biological Aspects of Signal Processing (2003)

    Google Scholar 

  34. Maio, D., Maltoni, D.: Real-time face location on gray-scale static images. Pattern Recognition 33, 1525–1539 (2000)

    Article  Google Scholar 

  35. Morris, J.S., Coombes, K.R., Koomen, J., Baggerly, K.A., Kobayashi, R.: Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum. Bioinformatics 21(9), 1764–1775 (2005)

    Article  Google Scholar 

  36. Na, S., Paek, E.: Quality assessment of tandem mass spectra based on cumulative intensity normalization. J. Proteome Res. 5(12), 3241–3248 (2006)

    Article  Google Scholar 

  37. Olofsson, P.: Probability, Statistics, and Stochastic Processes. John Wiley & Sons (2005)

    Google Scholar 

  38. Osuna, E., Freund, R., Girosi, F.: Training support vector machines: an application to face detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 130–136 (1997)

    Google Scholar 

  39. Osuna, E., Freund, R., Girosi, F.: Training Support Vector Machines: an application to face detection. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 130–136 (1997)

    Google Scholar 

  40. Petricoin, E.F., Ardekani, A.M., Levine, P.J., Hitt, B.A., Fusaro, V.A., Steinberg, S.M., Mills, G.B., Simone, C., Fishman, D.A., Kohn, E.C., Liotta, L.: Use of proteomic patterns in serum to identify ovarian cancer. The Lancet 359, 527–577 (2002)

    Article  Google Scholar 

  41. Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the Face Recognition Grand Challenge. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 947–954 (2005)

    Google Scholar 

  42. Phillips, P., Grother, P., Micheals, R., Blackburn, D., Tabassi, E., Bone, J.: Face Recognition Vendor Test 2002: Evaluation Report. NISTIR 6965 (2003)

    Google Scholar 

  43. Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.: The FERET database and evaluation procedure for face recognition algorithms. Image and Vision Computing J. 16(5), 295–306 (1998)

    Article  Google Scholar 

  44. Phillips, P.J.: Support vector machines applied to face recognition. In: Advances in Neural Information Processing Systems 11, pp. 803–809. MIT Press (1999)

    Google Scholar 

  45. Pietrowska, M., Marczak, L., Polanska, J., Behrendt, K., Nowicka, E., Walaszczyk, A., Chmura, A., Deja, R., Stobiecki, M., Polanski, A., Tarnawski, R., Widlak, P.: Mass spectrometry-based serum proteome pattern analysis in molecular diagnostics of early stage breast cancer. J. Transl. Med. 7, 60 (2009)

    Article  Google Scholar 

  46. Pietrowska, M., Marczak, L., Suwinski, R., Stobiecki, M., Polanska, J., Polanski, A., Widlak, P., Gawkowska-Suwinska, M., Drosik, A., Walaszczyk, A.: Application of mass spectrometry-based serum proteome pattern analysis in identification of lung cancer patients. J. Thorac. Oncol. 5(5, suppl. 1), S60 (2010); Abstract book, 2nd European Lung Cancer Conference, Geneva, Switzerland, April 28- May 1 (2010)

    Google Scholar 

  47. Polanska, J., Widnak, P., Rzeszowska-Wolny, J., Kimmel, M., Polanski, A.: Gaussian mixture decomposition of time-course dna microarray data. In: Mathematical Modeling of Biological Systems, Modeling and Simulation in Science, Engineering and Technology, vol. 1, pp. 351–359. Birkhäuser, Boston (2007)

    Google Scholar 

  48. Polanski, A., Kimmel, M.: Bioinformatics. Springer (2007)

    Google Scholar 

  49. Ralaivola, L., d’Alché-Buc, F.: Incremental Support Vector Machine Learning: A Local Approach. In: Dorffner, G., Bischof, H., Hornik, K. (eds.) ICANN 2001. LNCS, vol. 2130, pp. 322–330. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  50. Ratsch, G.: Accurate splice site detection for caenorhabditis elegans. In: Scholkopf, B., Tsuda, K., Vert, J.P. (eds.) Kernel Methods in Computational Biology, pp. 277–298. MIT Press (2004)

    Google Scholar 

  51. Roobaert, D.: DirectSVM: A fast and simple support vector machine perceptron. In: Proceedings of the 2000 IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing X, vol. 1, pp. 356–365. IEEE (2000)

    Google Scholar 

  52. Rowley, H.A., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 20(1), 23–38 (1998)

    Article  Google Scholar 

  53. Schölkopf, B., Bartlett, P.L., Smola, A.J., Williamson, R.C.: Shrinking the tube: A new support vector regression algorithm. In: Kearns, M.J., Solla, S.A., Cohn, D.A. (eds.) Advances in Neural Information Processing Systems 11, pp. 330–336. The MIT Press (1999)

    Google Scholar 

  54. Schölkopf, B., Smola, A.J., Williamson, R.C., Bartlett, P.L.: New support vector algorithms. Neural Computation 12(5), 1207–1245 (2000)

    Article  Google Scholar 

  55. Shalev-Shwartz, S., Srebro, N.: Svm optimization: inverse dependence on training set size. In: Proceedings of the 25th International Conference on Machine Learning, ICML 2008, New York, NY, USA, pp. 928–935 (2008)

    Google Scholar 

  56. Shin, H., Sampat, M.P., Koomen, J.M., Markey, M.K.: Wavelet-based adaptive denoising and baseline correction for maldi tof ms. OMICS 14(3), 283–295 (2010)

    Article  Google Scholar 

  57. Shipp, M.A., Ross, K.N., Tamayo, P., Weng, A.P., Aguiar, R.C.T., Kutok, J.L., Gaasenbeek, M., Angelo, M., Reich, M., Ray, T.S., Pinkus, G.S., Koval, M.A., Last, K.W., Norton, A., Mesirov, J., Lister, T.A., Neuberg, D.S., Lander, E.S., Aster, J.C., Gloub, T.R.: Diffuse large b-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature Medicine 8(1), 68–74 (2002)

    Article  Google Scholar 

  58. Pomeroy, S.L., Tamayo, P., Gaasenbeek, M., Sturla, L.M., Angelo, M., McLaughlin, M.E., Kim, J.Y.H., Goumnerova, L.C., Black, P.M., Lan, C., Allen, J.C., Zagzag, D., Olson, J.M., Curran, T., Wetmore, C., Biegel, J.A., Poggio, T., Mukherjee, S., Rifkin, R., Califano, A., Stolovitzky, G., Louis, D.N., Mesirov, J.P., Lander, E.S., Golub, T.R.: Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 415(24), 436–442 (2002)

    Article  Google Scholar 

  59. Steinwart, I.: Sparseness of support vector machines. J. Mach. Learn. Res. 4, 1071–1105 (2003)

    MathSciNet  Google Scholar 

  60. Tay, F.E.H., Cao, L.J.: Modified support vector machines in financial time series forecasting. Neurocomputing 48(1-4), 847–861 (2002)

    Article  MATH  Google Scholar 

  61. Turk, M., Pentland, A.: Face Recognition Using Eigenfaces. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–591 (1991)

    Google Scholar 

  62. van Eyk, J.E., Dunn, M.J.: Clinical Proteomics: From Diagnosis to Therapy. Wiley-VCH (2008)

    Google Scholar 

  63. Veer, L.J.V., Dai, H., van de Vijer, M.J., He, Y.D., Hart, A.A., Mao, M., Peterse, H.L., van der Kooy, K., Marton, M.J., Witteveen, A.T., Schreiber, G.j., Kerkhoven, R.M., Roberts, C., Linsley, P.S., Bernards, R., Friend, S.H.: Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(24), 530–536 (2002)

    Article  Google Scholar 

  64. Viola, P.A., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137–154 (2004)

    Article  Google Scholar 

  65. Vishwanathan, S., Murty, N., et al.: SSVM: a simple SVM algorithm. In: Proceedings of the 2002 International Joint Conference on Neural Networks, IJCNN 2002, vol. 3, pp. 2393–2398. IEEE (2002)

    Google Scholar 

  66. Wagner, M., Naik, D., Pothen, A.: Protocols for disease classification from mass spectrometry data. Proteomics 3(9), 1692–1698 (2003)

    Article  Google Scholar 

  67. Wilhelm, T., Bohme, H.-J., Gross, H.-M.: Classification of face images for gender, age, facial expression, and identity. In: ICANN, vol. (1), pp. 569–574 (2005)

    Google Scholar 

  68. Wiskott, L., Fellous, J., Kruger, N., Malsburg, C.: Face recognition by Elastic Bunch Graph Matching. Tech. Rep. IR-INI 96-08, Ruhr-Universitat Bochum, Germany (1996)

    Google Scholar 

  69. Wu, J., Smith, W.A.P., Hancock, E.R.: Facial gender classification using shape-from-shading. Image Vision Comput. 28(6), 1039–1048 (2010)

    Article  Google Scholar 

  70. Yambor, W., Draper, B., Beveridge, R.: Analyzing PCA-based face recognition algorithms: Eigenvector selection and distance measures. Empirical Evaluation Methods in Computer Vision (2002)

    Google Scholar 

  71. Yang, M.-H., Kriegman, D.J., Ahuja, N.: Detecting faces in images: A survey. IEEE Trans. Pattern Analysis and Machine Intelligence 24, 34–58 (2002)

    Article  Google Scholar 

  72. Yu, W., He, Z., Liu, J., Zhao, H.: Improving mass spectrometry peak detection using multiple peak alignment results. J. Proteome Res. 7(1), 123–129 (2008)

    Article  Google Scholar 

  73. Yu, W., Wu, B., Huang, T., Li, X., Williams, K., Zhao, H.: Statistical methods in proteomics. In: Pham, H. (ed.) Springer Handbook of Engineering Statistics, pp. 623–638. Springer (2006)

    Google Scholar 

  74. Zhang, C., Zhang, Z.: A survey of recent advances in face detection. Tech. rep., Microsoft Research (2010)

    Google Scholar 

  75. Zhao, W., Chellappa, R., Phillips, P., Rosenfeld, A.: Face recognition: A literature survey. Tech. Rep. CARTR-948, Center for Automation Research, University of Maryland, College Park (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Informatics, Gliwice, Poland

    Krzysztof A. Cyran, Jolanta Kawulok, Michal Kawulok, Magdalena Stawarz & Marcin Michalak

  2. Institute of Oncology, Gliwice, Poland

    Monika Pietrowska & Piotr Widłak

  3. Institute of Automatic Control, Gliwice, Poland

    Joanna Polańska

Authors
  1. Krzysztof A. Cyran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jolanta Kawulok
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michal Kawulok
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Magdalena Stawarz
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Marcin Michalak
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Monika Pietrowska
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Piotr Widłak
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Joanna Polańska
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Krzysztof A. Cyran .

Editor information

Editors and Affiliations

  1. Deptartment of Applied Computer Science, University of Winnipeg, 515 Portage Avenue, Winnipegg, R3B 2E9, Canada

    Sheela Ramanna

  2. , School of Electrical and Information, University of South Australia, Adelaide, SA 5095, Australia

    Lakhmi C Jain

  3. KES International, Shoreham-by-sea, BN43 9AF, United Kingdom

    Robert J. Howlett

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Cyran, K.A. et al. (2013). Support Vector Machines in Biomedical and Biometrical Applications. In: Ramanna, S., Jain, L., Howlett, R. (eds) Emerging Paradigms in Machine Learning. Smart Innovation, Systems and Technologies, vol 13. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28699-5_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-28699-5_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28698-8

  • Online ISBN: 978-3-642-28699-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation