A hybrid methodology for pattern recognition in signaling cervical cancer pathways

Authors:
David Escarcega;Fernando Ramos;Ana Espinosa;Jaime Berumen
Affiliations:
ITESM, Computer Science Department, Morelos, México;ITESM, Computer Science Department, Morelos, México;Hospital General de México, Unidad de Medicina Genómica, Ciudad de México, México;Hospital General de México, Unidad de Medicina Genómica, Ciudad de México, México
Venue:
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
Year:
2010

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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.