Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Find Key m/z Values in Predication of Mass Spectrometry Cancer Data

  • Conference paper
Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues (ICIC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5226))

Included in the following conference series:

Abstract

To find the significant biomarker is very important in detecting protein patterns associated with diseases. In this study multilevel wavelet analysis is performed on high dimensional mass spectrometry data to extract the detail coefficients, which are used to detect the difference between cancer tissue and normal tissue. In order to find the key m/z values of mass spectra, wavelet detail information is reconstructed based on orthogonal wavelet detail coefficients, and genetic algorithm is further employed to select best features from the reconstructed detail information. Finally the corresponding significant m/z values of mass spectra are identified using the optimized detail features.

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 189.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

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. Herrmann, P.C., Liotta, L.A., Petricoin, E.F.: Cancer Proteomics: The tate of the Art. Dis. Markers 17, 49–57 (2001)

    Google Scholar 

  2. Wright Jr., G.L., Cazares, L.H., Leung, S.M., Nasim, S., Adam, B.L., Yip, T.T., Schellhammer, P.F., Gong, L., Vlahou, A.: Protein Chip Surface Enhanced Laser Desorption/Ionization (SELDI) Mass Spectrometry: A Novel Protein Biochip Technology for Detection of Prostate Cancer Biomarkers in Complex Protein Mixtures. Prostate Cancer Prostatic Dis. 2, 264–276 (1999)

    Article  Google Scholar 

  3. Vlahou, A., Schellhammer, P.F., Mendrinos, S., Patel, K., Kondylis, F.I., Gong, L., Nasim, S., Wright, J.: Development of a Novel Proteomic Approach for the Detection of Transitional Cell Carcinoma of the Bladder in Urine. Am. J. Pathol. 158, 1491–1520 (2001)

    Google Scholar 

  4. Lilien, R.H., Farid, H., Donald, B.R.: Probabilistic Disease Classification of Expression-Dependent Proteomic Data from Mass Spectrometry of Human Serum. Computational Biology 10 (2003)

    Google Scholar 

  5. Wu, B., Abbott, T., Fishman, D., McMurray, W., Mor, G., Stone, K., Ward, D., Williams, K., Zhao, H.: Comparison of Statistical Methods for Classification of Ovarian Cancer Using Mass Spectrometry Data. Bioinformatics 19 (2003)

    Google Scholar 

  6. Jeffries, N.O.: Performance of a Genetic Algorithm for Mass Spectrometry Proteomics. BMC Bioinformatics 5 (2004)

    Google Scholar 

  7. Levner, I.: Feature Selection and Nearest Centroid Classification for Protein Mass Spectrometry. BMC Bioinformatics 6 (2005)

    Google Scholar 

  8. Yu, J.S., Ongarello, S., Fiedler, R., Chen, X.W., Toffolo, G., Cobelli, C., Trajanoski, Z.: Ovarian Cancer Identification Based on dimensionality Reduction for High-throughput Mass Spectrometry Data. Bioinformatics 21, 2200–2209 (2005)

    Article  Google Scholar 

  9. Liu, Y.: Feature Extraction for Mass Spectrometry Data. In: Li, K., Li, X., Irwin, G.W., He, G. (eds.) LSMS 2007. LNCS (LNBI), vol. 4689, pp. 188–196. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  10. Liu, Y.: Cancer Classification Based on Mass Spectrometry. In: Masulli, F., Mitra, S., Pasi, G. (eds.) WILF 2007. LNCS (LNAI), vol. 4578, pp. 596–603. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  11. Mallat, S.: A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 674–693 (1989)

    Article  MATH  Google Scholar 

  12. Daubechies, I.: Orthonormal Bases of Compactly Supported Wavelets. Communications on Pure and Applied Mathematics 41, 909–996 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  13. Holland, J.H.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge (1992)

    Google Scholar 

  14. Blanco, M., Delgado, M.C.: Pegalajar: A Real-coded Genetic Algorithm for Training Recurrent Neural Networks. Neural Networks 14, 93–105 (2001)

    Article  Google Scholar 

  15. Herrera, F., Lozano, M., Verdegay, J.L.: Tackling Real-coded Genetic Algorithms: Operators and Tools for Behavioral Analysis. Artif. Intell. Rev. 12, 265–319 (1998)

    Article  MATH  Google Scholar 

  16. Petricoin, E.F., Ornstein, D.K., Paweletz, C.P., Ardekani, A., Hackett, P.S., Hitt, B.A., Velassco, A., Trucco, C., Wiegand, L., Wood, K., Simone, C.B., Levine, P.J., Linehan, W.M., Emmert-Buck, M.R., Steinberg, S.M., Kohn, E.C., Liotta, L.A.: Serum Proteomic Patterns for Detection of Prostate Cancer. J. Natl. Cancer Inst. 94, 1576–1578 (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Information Technology, Shandong Institute of Light Industry, Jinan, Shandong, China, 250353

    Yihui Liu

  2. School of Computer Science, University of Nottingham, Jubilee Campus, Nottingham, UK, NG8 1BB

    Li Bai

Authors
  1. Yihui Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Automation, University of Science and Technology of China, 230026, Hefei, Anhui, China

    De-Shuang Huang

  2. Applied Computational Intelligence Laboratory, P.O. Box

    Donald C. Wunsch II

  3. Arlington, USA

    Daniel S. Levine

  4. Graduate School of Electrical Engineering, University of Ulsan, Korea, San 29, Mugeo-Dong, 680 - 749, Nam-Ku, Ulsan, Korea

    Kang-Hyun Jo

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Liu, Y., Bai, L. (2008). Find Key m/z Values in Predication of Mass Spectrometry Cancer Data. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2008. Lecture Notes in Computer Science, vol 5226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87442-3_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-87442-3_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87440-9

  • Online ISBN: 978-3-540-87442-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation