Integration of 3D gene expression patterns and gene regulatory networks for clinical applications in epithelial ovarian cancer

DOIResolve DOI: http://doi.org/10.1109/BHI.2016.7455954
AuthorSearch for: ; Search for: ; Search for:
TypeArticle
Proceedings title2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)
Conference2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 24-27 Feb., 2016, Las Vegas, NV
ISBN978-1-5090-2455-1
Pages541544
AbstractIn the past decades, many high-throughput studies have been performed to investigate molecular mechanisms underlying epithelial ovarian cancer (EOC), to improve treatments and to develop early detection and staging biomarkers. EOC is still a deadly disease due in part to a lack of screening tools and to the absence of subtype and stage-specific targeted treatments. Here, we applied an integrative three-dimensional clustering algorithm to analyze gene expression data from normal ovaries and four subtypes of EOC. Our analysis revealed major differences between subtypes and highlighted biological patterns linked with stages of the disease. These results may contribute to the understanding of molecular mechanisms underlying EOC and find applications in EOC detection and treatment.
Publication date
PublisherIEEE
LanguageEnglish
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number23000348
Export citationExport as RIS
Report a correctionReport a correction
Record identifiera233c394-6360-4bb1-8719-15d6a89bf65f
Record created2016-07-08
Record modified2016-07-08
Bookmark and share
  • Share this page with Facebook (Opens in a new window)
  • Share this page with Twitter (Opens in a new window)
  • Share this page with Google+ (Opens in a new window)
  • Share this page with Delicious (Opens in a new window)