ATHENA: Identifying interactions between different levels of genomic data associated with cancer clinical outcomes using grammatical evolution neural network

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@Article{journals/biodatamining/KimLDR13,
  title =        "{ATHENA}: Identifying interactions between different
                 levels of genomic data associated with cancer clinical
                 outcomes using grammatical evolution neural network",
  author =       "Dokyoon Kim and Ruowang Li and Scott M. Dudek and 
                 Marylyn D. Ritchie",
  journal =      "BioData Mining",
  year =         "2013",
  volume =       "6",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, GE, Integrative analysis, Multi-omics data,
                 Grammatical evolution neural network, Ovarian cancer",
  bibdate =      "2014-01-24",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/biodatamining/biodatamining6.html#KimLDR13",
  URL =          "http://dx.doi.org/10.1186/1756-0381-6-23",
  size =         "14 pages",
  abstract =     "Background

                 Gene expression profiles have been broadly used in
                 cancer research as a diagnostic or prognostic signature
                 for the clinical outcome prediction such as stage,
                 grade, metastatic status, recurrence, and patient
                 survival, as well as to potentially improve patient
                 management. However, emerging evidence shows that gene
                 expression-based prediction varies between independent
                 data sets. One possible explanation of this effect is
                 that previous studies were focused on identifying genes
                 with large main effects associated with clinical
                 outcomes. Thus, non-linear interactions without large
                 individual main effects would be missed. The other
                 possible explanation is that gene expression as a
                 single level of genomic data is insufficient to explain
                 the clinical outcomes of interest since cancer can be
                 dysregulated by multiple alterations through genome,
                 epigenome, transcriptome, and proteome levels. In order
                 to overcome the variability of diagnostic or prognostic
                 predictors from gene expression alone and to increase
                 its predictive power, we need to integrate multi-levels
                 of genomic data and identify interactions between them
                 associated with clinical outcomes.

                 Results

                 Here, we proposed an integrative framework for
                 identifying interactions within/between multi-levels of
                 genomic data associated with cancer clinical outcomes
                 using the Grammatical Evolution Neural Networks (GENN).
                 In order to demonstrate the validity of the proposed
                 framework, ovarian cancer data from TCGA was used as a
                 pilot task. We found not only interactions within a
                 single genomic level but also interactions between
                 multi-levels of genomic data associated with survival
                 in ovarian cancer. Notably, the integration model from
                 different levels of genomic data achieved 72.89percent
                 balanced accuracy and outperformed the top models with
                 any single level of genomic
                 data.

                 Conclusions

                 Understanding the underlying tumorigenesis and
                 progression in ovarian cancer through the global view
                 of interactions within/between different levels of
                 genomic data is expected to provide guidance for
                 improved prognostic biomarkers and individual
                 therapies.",
}

Genetic Programming entries for Dokyoon Kim Ruowang Li Scott M Dudek Marylyn D Ritchie

Citations