Knowledge-driven genomic interactions: an application in ovarian cancer

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@Article{Kim:2014:bdm,
  author =       "Dokyoon Kim and Ruowang Li and Scott M Dudek and 
                 Alex T Frase and Sarah A Pendergrass and Marylyn D Ritchie",
  title =        "Knowledge-driven genomic interactions: an application
                 in ovarian cancer",
  journal =      "BioData Mining",
  year =         "2014",
  volume =       "7",
  number =       "20",
  keywords =     "genetic algorithms, genetic programming,
                 knowledge-driven genomic interaction, integrative
                 analysis, grammatical evolution neural network,
                 clinical outcome prediction, ovarian cancer",
  ISSN =         "1756-0381",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.463.5223",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.463.5223",
  URL =          "http://www.biodatamining.org/content/pdf/1756-0381-7-20.pdf",
  DOI =          "doi:10.1186/1756-0381-7-20",
  size =         "11 pages",
  abstract =     "Background

                 Effective cancer clinical outcome prediction for
                 understanding of the mechanism of various types of
                 cancer has been pursued using molecular-based data such
                 as gene expression profiles, an approach that has
                 promise for providing better diagnostics and supporting
                 further therapies. However, clinical outcome prediction
                 based on gene expression profiles varies between
                 independent data sets. Further, single-gene expression
                 outcome prediction is limited for cancer evaluation
                 since genes do not act in isolation, but rather
                 interact with other genes in complex signalling or
                 regulatory networks. In addition, since pathways are
                 more likely to co-operate together, it would be
                 desirable to incorporate expert knowledge to combine
                 pathways in a useful and informative
                 manner.

                 Methods

                 Thus, we propose a novel approach for identifying
                 knowledge-driven genomic interactions and applying it
                 to discover models associated with cancer clinical
                 phenotypes using grammatical evolution neural networks
                 (GENN). In order to demonstrate the utility of the
                 proposed approach, an ovarian cancer data from the
                 Cancer Genome Atlas (TCGA) was used for predicting
                 clinical stage as a pilot project.

                 Results

                 We identified knowledge-driven genomic interactions
                 associated with cancer stage from single knowledge
                 bases such as sources of pathway-pathway interaction,
                 but also knowledge-driven genomic interactions across
                 different sets of knowledge bases such as
                 pathway-protein family interactions by integrating
                 different types of information. Notably, an integration
                 model from different sources of biological knowledge
                 achieved 78.82percent balanced accuracy and
                 outperformed the top models with gene expression or
                 single knowledge-based data types alone. Furthermore,
                 the results from the models are more interpretable
                 because they are framed in the context of specific
                 biological pathways or other expert
                 knowledge.

                 Conclusions

                 The success of the pilot study we have presented herein
                 will allow us to pursue further identification of
                 models predictive of clinical cancer survival and
                 recurrence. Understanding the underlying tumourigenesis
                 and progression in ovarian cancer through the global
                 view of interactions within/between different
                 biological knowledge sources has the potential for
                 providing more effective screening strategies and
                 therapeutic targets for many types of cancer.",
  notes =        "Department of Biochemistry and Molecular Biology,
                 Center for Systems Genomics, Pennsylvania State
                 University, University Park, Pennsylvania, USA",
}

Genetic Programming entries for Dokyoon Kim Ruowang Li Scott M Dudek Alex T Frase Sarah A Pendergrass Marylyn D Ritchie

Citations