A Hybrid Methodology for Pattern Recognition in Signaling Cervical Cancer Pathways

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.

[1]  A. Barabasi,et al.  The human disease network , 2007, Proceedings of the National Academy of Sciences.

[2]  Benno Schwikowski,et al.  Graph-based methods for analysing networks in cell biology , 2006, Briefings Bioinform..

[3]  D. Koller,et al.  From signatures to models: understanding cancer using microarrays , 2005, Nature Genetics.

[4]  David Zhang,et al.  The crosstalk between EGF, IGF, and Insulin cell signaling pathways - computational and experimental analysis , 2009, BMC Systems Biology.

[5]  K. Becker,et al.  Analysis of microarray data using Z score transformation. , 2003, The Journal of molecular diagnostics : JMD.

[6]  Satoru Miyano,et al.  Inferring gene networks from time series microarray data using dynamic Bayesian networks , 2003, Briefings Bioinform..

[7]  B Marshall,et al.  Gene Ontology Consortium: The Gene Ontology (GO) database and informatics resource , 2004, Nucleic Acids Res..

[8]  Alfonso Valencia,et al.  Implementing the iHOP concept for navigation of biomedical literature , 2005, ECCB/JBI.

[9]  L. Mirny,et al.  Protein complexes and functional modules in molecular networks , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Harald zur Hausen,et al.  Papillomaviruses in the causation of human cancers - a brief historical account. , 2009, Virology.

[11]  Kiyoko F. Aoki-Kinoshita,et al.  From genomics to chemical genomics: new developments in KEGG , 2005, Nucleic Acids Res..

[12]  Ian Stark,et al.  The Continuous pi-Calculus: A Process Algebra for Biochemical Modelling , 2008, CMSB.

[13]  A. Barabasi,et al.  Network biology: understanding the cell's functional organization , 2004, Nature Reviews Genetics.

[14]  S. Krishna,et al.  Cell intrinsic & extrinsic factors in cervical carcinogenesis. , 2009, The Indian journal of medical research.

[15]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[16]  T. Golub,et al.  DNA microarrays in clinical oncology. , 2002, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[17]  Edward R. Dougherty,et al.  Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks , 2002, Bioinform..

[18]  Seon-Young Kim,et al.  Gene-set approach for expression pattern analysis , 2008, Briefings Bioinform..

[19]  Hedi Peterson,et al.  Gene expression KEGGanim : pathway animations for high-throughput data , 2008 .

[20]  Monika Heiner,et al.  A Unifying Framework for Modelling and Analysing Biochemical Pathways Using Petri Nets , 2007, CMSB.

[21]  Michael L. Mavrovouniotis,et al.  Petri Net Representations in Metabolic Pathways , 1993, ISMB.

[22]  Taewon Lee,et al.  A method for computing the overall statistical significance of a treatment effect among a group of genes , 2006, BMC Bioinformatics.

[23]  D R Westhead,et al.  Petri Net representations in systems biology. , 2003, Biochemical Society transactions.

[24]  Monika Heiner,et al.  Petri Net Based Model Validation in Systems Biology , 2004, ICATPN.

[25]  Wolfgang Reisig,et al.  Applications and Theory of Petri Nets 2004 , 2004, Lecture Notes in Computer Science.

[26]  Rafael A Irizarry,et al.  Exploration, normalization, and summaries of high density oligonucleotide array probe level data. , 2003, Biostatistics.

[27]  Atsushi Doi,et al.  Biopathways representation and simulation on hybrid functional Petri net , 2003, Silico Biol..

[28]  Stefan Wiemann,et al.  KEGGgraph: a graph approach to KEGG PATHWAY in R and bioconductor , 2009, Bioinform..

[29]  Ulrike Schmidt,et al.  SuperMimic – Fitting peptide mimetics into protein structures , 2006, BMC Bioinformatics.