Skip to main content

Advertisement

SpringerLink
Log in

Analysis of mass spectrometry data of cerebral stroke samples: an evolutionary computation approach to resolve and quantify peptide peaks

  • Original Paper
  • Published:
Genetic Programming and Evolvable Machines Aims and scope Submit manuscript

Abstract

A preliminary investigation of cerebral stroke samples injected into a mass spectrometer is performed from an evolutionary computation perspective. The detection and resolution of peptide peaks is pursued for the purpose of automatically and accurately determining unlabeled peptide quantities. A theoretical peptide peak model is proposed and a series of experiments are then pursued (most within a distributed computing environment) along with a data preprocessing strategy that includes (i) a deisotoping step followed by (ii) a peak picking procedure, followed by (iii) a series of evolutionary computation experiments oriented towards the investigation of their capability for achieving the aforementioned goal. Results from four different genetic algorithms (GA) and one differential evolution (DE) algorithm are reported with respect to their ability to find solutions that fit within the framework of the presented theoretical peptide peak model. Both unconstrained and constrained (as determined by a course grained preprocessing stage) solution space experiments are performed for both types of evolutionary algorithms. Good preliminary results are obtained.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. T. Masters, Advanced Algorithms for Neural Networks. (Wiley, 1993)

  2. U. Schioler, H. Hartmann, Mapping neural networks derived from the parzen window estimator. Neural Networks 5(6), 903–909 (1992)

    Article  Google Scholar 

  3. G. Palacky, G. West, Computer processing of airbone electromagnetic data. Geophys. Prospect. 22(3), 490–509 (1974)

    Article  Google Scholar 

  4. D.E. Goldberg, B. Korb, K. Deb, Messy genetic algorithms: motivation, analysis, and first results. Complex Systems 3, 93–530 (1989)

    MathSciNet  Google Scholar 

  5. D.E. Goldberg, K. Deb, B. Korb, Messy genetic algorithms revisited: nonuniform size and scale. Complex Systems 4, 415–444, (1990)

    MATH  Google Scholar 

  6. D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. (Addison-Wesley, 1989)

  7. T. Bäck, D.B. Fogel, Z. Michalewicz, Handbook of Evolutionary Computation. (Institute of Physics Publishing and Oxford University Press, New York, Oxford, 1997)

    MATH  Google Scholar 

  8. T. Bäck, D.B. Fogel, Z. Michalewicz, Evolutionary Computation 1. Basic Algorithms and Operators. (Institute of Physics Publishing, Bristol and Philadelphia, 2000).

    MATH  Google Scholar 

  9. T. Bäck, D.B. Fogel, Z. Michalewicz, Evolutionary Computation 2. Advanced Algorithms and Operators. (Institute of Physics Publishing, Bristol and Philadelphia, 2000)

    MATH  Google Scholar 

  10. T. Wall. GaLib: A C++ Library of Genetic Algorithm Components. Mechanical Engineering Dept. MIT, http://www.lancet.mit.edu/ga/ (1996)

  11. R. Storn, K. Price, Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, ICSI, March 1995

  12. K. Price, Differential evolution: a fast and simple numerical optimizer, in 1996 Biennial Conference of the North American Fuzzy Information Processing Society, NAFIPS, ed. by J.K.J.Y.M. Smith, M. Lee (IEEE Press, New York, 1996), pp. 524–527

    Chapter  Google Scholar 

  13. R.S.K. Price, J. Lampinen, Differential Evolution: A Practical Approach to Global Optimization. Natural Computing Series. (Springer Verlag, 2005)

  14. D. Thain, T. Tannenbaum, M. Livny, Distributed computing in practice: the condor experience. Concurrency—Pract. Ex. 17(2–4):323–356, (2005)

    Google Scholar 

  15. D. Thain, M. Livny, Building reliable clients and servers, in The Grid: Blueprint for a New Computing Infrastructure, ed. by I. Foster, C. Kesselman (Morgan Kaufmann, 2003)

  16. D. Thain, T. Tannenbaum, M. Livny, Condor and the grid, in Grid Computing: Making the Global Infrastructure a Reality, ed. by F. Berman, G. Fox, T. Hey (Wiley, 2002)

  17. P. Brockwell, R. Davis, Time Series: Theory and Methods. Springer Series in Statistics. (Springer, 1986)

Download references

Acknowledgments

The authors would like to thank Jeff Cameron from the University of Waterloo for his help with the numeric experiments, and to Robert Orchard from the Integrated Reasoning Group (National Research Council Canada, Institute for Information Technology) for his constructive criticism of the first draft of this paper.

Author information

Authors and Affiliations

  1. Institute for Information Technology, National Research Council Canada, Bldg. M-50, 1200 Montreal Rd., Ottawa, ON, Canada, K1A 0R6

    Julio J. Valdés & Alan J. Barton

  2. Institute for Biological Sciences, National Research Council Canada, 100 Sussex Dr., Ottawa, ON, Canada, K1A 0R6

    Arsalan S. Haqqani

Authors
  1. Julio J. Valdés
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alan J. Barton
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arsalan S. Haqqani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alan J. Barton.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Valdés, J.J., Barton, A.J. & Haqqani, A.S. Analysis of mass spectrometry data of cerebral stroke samples: an evolutionary computation approach to resolve and quantify peptide peaks. Genet Program Evolvable Mach 9, 257–274 (2008). https://doi.org/10.1007/s10710-008-9057-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10710-008-9057-y

Keywords

Search

Navigation