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Nonlinear Recognition Methods for Oncological Pathologies

In: Data Mining for Biomarker Discovery

Author

Listed:
  • Gregorio Patrizi

    (“Sapienza”-University of Rome)

  • Vincenzo Pietropaolo

    (“Sapienza”-University of Rome)

  • Antonella Carbone

    (“Sapienza”-University of Rome)

  • Renato Leone

    (Universitá di Camerino)

  • Laura Giacomo

    (Probabilita e Statistiche Applicate)

  • Valentina Losacco

    (Probabilita e Statistiche Applicate)

  • Giacomo Patrizi

    (Probabilita e Statistiche Applicate)

Abstract

A biomarker, or biological marker is a substance used as an indicator of a biological state. It is used in many scientific fields. The determination and function of the biomarker can be formalized more precisely by using Nonlinear Recognition Methods for accurate identification of oncological pathologies and both the pathogenic processes and pharmacologic response to a therapeutic intervention by applying dynamical systems and chaotic algorithms to determine the biological state and its dynamics. To this end a classification problem is solved based on optimal nonlinear algorithm, and it will be shown that certainty equivalent predictions are derived. Application results will be given on available test data sets of gastroscopic and colonoscopic images. The increase in the recognition accuracy is attributable to the algorithm and a strict statistical methodology without extraneous assumptions.

Suggested Citation

  • Gregorio Patrizi & Vincenzo Pietropaolo & Antonella Carbone & Renato Leone & Laura Giacomo & Valentina Losacco & Giacomo Patrizi, 2012. "Nonlinear Recognition Methods for Oncological Pathologies," Springer Optimization and Its Applications, in: Panos M. Pardalos & Petros Xanthopoulos & Michalis Zervakis (ed.), Data Mining for Biomarker Discovery, edition 127, chapter 0, pages 169-185, Springer.
    • Handle: RePEc:spr:spochp:978-1-4614-2107-8_9
    DOI: 10.1007/978-1-4614-2107-8_9
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