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dc.contributor.authorZaka, Masood-Ul-Hassan*
dc.contributor.authorPeng, Yonghong*
dc.contributor.authorSutton, Chris W.*
dc.date.accessioned2016-09-21T15:46:32Z
dc.date.available2016-09-21T15:46:32Z
dc.date.issued2015
dc.identifier.citationZaka MUH, Peng Y and Sutton CW (2015) Microarray big data integrated analysis to identify robust diagnostic signature for triple negative breast cancer. In: Proceedings of the 2014 IEEE Symposium on Computational Intelligence in Big Data. 9-12 December 2014. Orlando, Fl, USA.
dc.identifier.urihttp://hdl.handle.net/10454/9254
dc.descriptionNo
dc.description.abstractTriple negative breast cancers (TNBC) are clinically heterogeneous, an aggressive subtype with poor diagnosis and strong resistance to therapy. There is a need to identify novel robust biomarkers with high specificity for early detection and therapeutic intervention. Microarray gene expression-based studies have offered significant advances in molecular classification and identification of diagnostic/prognostic signatures, however sample scarcity and cohort heterogeneity remains area of concern. In this study, we performed integrated analysis on independent microarray big data studies and identified a robust 880-gene signature for TNBC diagnosis. We further identified 16-gene (OGN, ESR1, GPC3, LHFP, AGR3, LPAR1, LRRC17, TCEAL1, CIRBP, NTN4, TUBA1C, TMSB10, RPL27, RPS3A, RPS18, and NOSTRIN) that are associated to TNBC tissues. The 880-gene signature achieved excellent classification accuracy ratio on each independent expression data sets with overall average of 99.06%, is an indication of its diagnostic power. Gene ontology enrichment analysis of 880-gene signature shows that cell-cycle pathways/processes are important clinical targets for triple negative breast cancer. Further verification of 880-gene signature could provide additive knowledge for better understanding and future direction of triple negative breast cancer research.
dc.relation.isreferencedbyhttp://dx.doi.org/10.1109/CIBD.2014.7011529
dc.subjectCancer; Gene expression; Big data; Pattern classification; Biology computing; Genetics; Molecular biophysics; Microarray; Early detection
dc.titleMicroarray big data integrated analysis to identify robust diagnostic signature for triple negative breast cancer
dc.status.refereedYes
dc.typeConference Paper
dc.type.versionNo full-text available in the repository


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