saini-breastcancer-2014.pdf (2.25 MB)
Breast cancer prognosis risk estimation using integrated gene expression and clinical data
journal contribution
posted on 2014-01-01, 00:00 authored by Ashish Saini, Jingyu HouJingyu Hou, Wanlei ZhouNovel prognostic markers are needed so newly diagnosed breast cancer patients do not undergo any unnecessary therapy. Various microarray gene expression datasets based studies have generated gene signatures to predict the prognosis outcomes, while ignoring the large amount of information contained in established clinical markers. Nevertheless, small sample sizes in individual microarray datasets remain a bottleneck in generating robust gene signatures that show limited predictive power. The aim of this study is to achieve high classification accuracy for the good prognosis group and then achieve high classification accuracy for the poor prognosis group.
History
Journal
BioMed research internationalVolume
2014Article number
459203Pagination
1 - 15Publisher
Hindawi Publishing CorporationLocation
United StatesPublisher DOI
ISSN
2314-6133eISSN
2314-6141Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2014, Hindawi Publishing CorporationUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC