A Hybrid Methodology for Pattern Recognition in Signaling Cervical Cancer Pathways

  • David Escarcega
  • Fernando Ramos
  • Ana Espinosa
  • Jaime Berumen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6256)

Abstract

Cervical Cancer (CC) is the result of the infection of high risk Human Papilloma Viruses. mRNA microarray expression data provides biologists with evidences of cellular compensatory gene expression mechanisms in the CC progression. Pattern recognition of signalling pathways through expression data can reveal interesting insights for the understanding of CC. Consequently, gene expression data should be submitted to different pre-processing tasks. In this paper we propose a methodology based on the integration of expression data and signalling pathways as a needed phase for the pattern recognition within signaling CC pathways. Our results provide a top-down interpretation approach where biologists interact with the recognized patterns inside signalling pathways.

Keywords

Cervical Cancer Gene Expression Data MAPK Signaling Pathway Boolean Network Cervical Carcinogenesis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • David Escarcega
    • 1
  • Fernando Ramos
    • 1
  • Ana Espinosa
    • 2
  • Jaime Berumen
    • 2
  1. 1.Computer Science DepartmentITESMMorelosMéxico
  2. 2.Hospital General de MéxicoUnidad de Medicina GenómicaCiudad de MéxicoMéxico

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