Group-biomarkers identification in ovarian carcinoma

Alain B. Tchagang; Ahmed H. Tewfik; Amy P.N. Skubitz; Keith Skubitz

doi:10.1109/ICASSP.2007.366686

Group-biomarkers identification in ovarian carcinoma

Alain B. Tchagang
, Ahmed H. Tewfik
, Amy P.N. Skubitz
, Keith Skubitz

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Scopus citations

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 hiomarkers as exhibited by receiver operating characteristics curves.

Original language	English (US)
Title of host publication	2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Pages	I341-I344
DOIs	https://doi.org/10.1109/ICASSP.2007.366686
State	Published - Aug 6 2007
Event	2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States Duration: Apr 15 2007 → Apr 20 2007

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	1
ISSN (Print)	1520-6149

Other

Other	2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Country/Territory	United States
City	Honolulu, HI
Period	4/15/07 → 4/20/07

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Biclustering
Biomarkers
DNA microarray
Ovarian cancer

Publisher link

10.1109/ICASSP.2007.366686

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Tchagang, A. B., Tewfik, A. H., Skubitz, A. P. N., & Skubitz, K. (2007). Group-biomarkers identification in ovarian carcinoma. In 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 (pp. I341-I344). Article 4217086 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 1). https://doi.org/10.1109/ICASSP.2007.366686

Group-biomarkers identification in ovarian carcinoma. / Tchagang, Alain B.; Tewfik, Ahmed H.; Skubitz, Amy P.N. et al.
2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07. 2007. p. I341-I344 4217086 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Tchagang, AB, Tewfik, AH, Skubitz, APN & Skubitz, K 2007, Group-biomarkers identification in ovarian carcinoma. in 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07., 4217086, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 1, pp. I341-I344, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07, Honolulu, HI, United States, 4/15/07. https://doi.org/10.1109/ICASSP.2007.366686

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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 hiomarkers as exhibited by receiver operating characteristics curves.",

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AU - Tchagang, Alain B.

AU - Tewfik, Ahmed H.

AU - Skubitz, Amy P.N.

AU - Skubitz, Keith

PY - 2007/8/6

Y1 - 2007/8/6

N2 - 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 hiomarkers as exhibited by receiver operating characteristics curves.

AB - 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 hiomarkers as exhibited by receiver operating characteristics curves.

KW - Biclustering

KW - Biomarkers

KW - DNA microarray

KW - Ovarian cancer

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ER -

Group-biomarkers identification in ovarian carcinoma

Abstract

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UN SDGs

Keywords

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