- 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",
