DocumentCode :
463459
Title :
Group-Biomarkers Identification in Ovarian Carcinoma
Author :
Tchagang, Alain B. ; Tewfik, Ahmed H. ; Skubitz, Amy P N ; Skubitz, Keith
Author_Institution :
Dept. of Biomed. Eng., Minnesota Univ., Minneapolis, MN
Volume :
1
fYear :
2007
fDate :
15-20 April 2007
Abstract :
In this paper, we propose group-biomarkers as an alternative to the traditional single biomarkers used to date for the detection of ovarian cancer. Group-biomarkers are a set of genes that are used simultaneously for the diagnosis of early-stage and/or recurrent cancer. We describe a procedure for identifying such group-biomarkers from a data set of gene expression levels corresponding to normal and diseased ovarian tissue as well as tissue from other organs. The procedure starts with a list of potential single biomarkers. It then uses an order preserving biclustering step to identify other genes that are co-regulated with the candidate single biomarkers across the normal and diseased ovarian tissue and tissue from other organ. We present a statistical analysis that demonstrates that group-biomarkers have a much better detection performance than single biomarkers as exhibited by receiver operating characteristics curves.
Keywords :
biological tissues; cancer; genetics; medical image processing; statistical analysis; gene expression levels; group-biomarkers identification; order preserving biclustering; ovarian cancer detection; ovarian carcinoma; ovarian tissue; statistical analysis; Biomarkers; Biomedical engineering; Cancer detection; Chemical analysis; Diseases; Gene expression; Medical treatment; Neoplasms; Statistical analysis; Testing; Biclustering; DNA microarray; biomarkers; ovarian cancer;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on
Conference_Location :
Honolulu, HI
ISSN :
1520-6149
Print_ISBN :
1-4244-0727-3
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2007.366686
Filename :
4217086
Link To Document :
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