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Hybridization of Blind Source Separation and Rough Sets for Proteomic Biomarker Indentification

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Artificial Intelligence and Soft Computing - ICAISC 2004 (ICAISC 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3070))

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Abstract

Biomarkers are molecular parameters associated with presence and severity of specific disease states. Search for biological markers of cancer in proteomic profiles is a relatively new but very active research area. This paper presents a novel approach to feature selection and thus biomarker identification. The proposed method is based on blind separation of sources and selection of features from a reduced set of components.

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References

  1. Srinivas, P.R., Srivastava, S., Hanash, S., Wright Jr., G.L.: Proteomics in early detection of cancer. Clinical Chemistry 47, 1901–1911 (2001)

    Google Scholar 

  2. Conrads, T.P., Zhou, M., III, E.F.P., Liotta, L., Veenstra, T.D.: Cancer diagnosis using proteomic patterns. Future Drugs 4, 411 – 420 (2003)

    Google Scholar 

  3. Diamandis, E.P.: Proteomic patterns in bilogical fluids: do they represent the future of cancer diagnostics? Clinical Chemistry 8, 1272–1278 (2003)

    Article  Google Scholar 

  4. Wulfkuhle, J.D., Liotta, L.A., Petricoin, E.F.: Proteomic applications for the early detection of cancer. Nature Reviews 3, 267–275 (2003)

    Google Scholar 

  5. Sorace, J.M., Zahn, M.: A data review and re-assesment of ovarian cancer serum proteomic profiling. BMC Bioinformatics 4, 1471–2105 (2003)

    Article  Google Scholar 

  6. Yasui, Y., Pepe, M., Thompson, M.L., Adam, B.-L., Wright, J. G.L., Qu, Y., Potter, J.D., Winget, M., Thornquist, M., Feng, Z.: A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection. Biostatistics 4, 449–463 (2003)

    Article  MATH  Google Scholar 

  7. Petricoin, E.F., Ardekani, A.M., Hitt, B.A., Levine, P.J., Fusaro, V.A., Steinberg, S.M., Mills, G.B., Simone, C., Fishman, D.A., Kohn, E.C., Liotta, L.A.: Use of proteomic patterns in serum to identify ovarian cancer. Lancet 359, 572–577 (2002)

    Article  Google Scholar 

  8. Boratyn, G.M., Smolinski, T.G., Milanova, M., Zurada, J.M., Bhattacharyya, S., Suva, L.J.: Scoring-based analysis of protein patterns for identification of myeloma cancer. In: Valafar, F., Valafar, H. (eds.) Proceedings of the International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences, METMBS 2003, Las Vegas, NV, June 23 – 26, pp. 60–65 (2003)

    Google Scholar 

  9. Boratyn, G.M., Smolinski, T.G., Milanova, M., Zurada, J.M., Bhattacharyya, S., Suva, L.J.: Bayesian approach to analysis of protein patterns for identification of myeloma cancer. In: Proceeding of the Second International Conference on Machine Learning and Cybernetics, Xi-an, November 2 – 5, pp. 1217–1221. IEEE, Los Alamitos (2003)

    Google Scholar 

  10. Blum, A., Langley, P.: Selection of relevant features and examples in machine learning. Artificail Intelligence 97, 245–271 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  11. Olshausen, B.A.: Sparse codes and spikes. In: Rao, R.P.N., Olshausen, B.A., Lewicki, M.S. (eds.) Probabilistic models of perception and brain function, pp. 257–272. MIT Press, Cambridge (2001)

    Google Scholar 

  12. Amari, S.-I., Cichocki, A.: Adaptive blind signal processing - neural network approaches. Proc. of the IEEE 86, 2026–2048 (1998)

    Article  Google Scholar 

  13. Comon, P.: Independent component analysis - a new concept? Signal Processing 36, 287–314 (1994)

    Article  MATH  Google Scholar 

  14. Cover, T.M., Thomas, J.A.: Elements of information theory. John Wiley and Sons, Inc., New York (1991)

    Book  MATH  Google Scholar 

  15. Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  16. Marek, W., Pawlak, Z.: Rough sets and information systems. Fundamenta Matematicae 17, 105–115 (1984)

    MathSciNet  Google Scholar 

  17. Komorowski, J., Pawlak, Z., Polkowski, L., Skowron, A.: Rough sets: A tutorial. In: Pal, S.K., Skowron, A. (eds.) Rough Fuzzy Hybridization - A New Trend in Decision-Making, pp. 3–98 (1999)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY

    Grzegorz M. Boratyn, Tomasz G. Smolinski & Jacek M. Zurada

  2. Department of Computer Science, University of Arkansas at Little Rock, Little Rock, AR

    Mariofanna Milanova

  3. Department of Orthopedic Surgery, Barton Research Institute, University of Arkansas for Medical Sciences, Little Rock, AR

    Sudeepa Bhattacharyya

  4. Department of Physiology and Biophysics, Barton Research Institute, University of Arkansas for Medical Sciences, Little Rock, AR

    Larry J. Suva

Authors
  1. Grzegorz M. Boratyn
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  2. Tomasz G. Smolinski
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  3. Jacek M. Zurada
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  4. Mariofanna Milanova
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  5. Sudeepa Bhattacharyya
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  6. Larry J. Suva
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. German Research Center of Artificial Intelligence (DFKI), Germany

    Jörg H. Siekmann

  3. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Initiative in Soft Computing (BISC), 94720-1776, Berkeley, CA

    Lotfi A. Zadeh

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© 2004 Springer-Verlag Berlin Heidelberg

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Boratyn, G.M., Smolinski, T.G., Zurada, J.M., Milanova, M., Bhattacharyya, S., Suva, L.J. (2004). Hybridization of Blind Source Separation and Rough Sets for Proteomic Biomarker Indentification. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_72

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  • DOI: https://doi.org/10.1007/978-3-540-24844-6_72

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22123-4

  • Online ISBN: 978-3-540-24844-6

  • eBook Packages: Springer Book Archive

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