- 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.",
