Integration of clinical information and gene expression profiles for prediction of chemo-response for ovarian cancer

Lihua Li; Li Chen; D. Goldgof; F. George; Z. Chen; A. Rao; J. Cragun; R. Sutphen; Johnathan M. Lancaster

Integration of clinical information and gene expression profiles for prediction of chemo-response for ovarian cancer

Lihua Li, Li Chen, D. Goldgof, F. George, Z. Chen, A. Rao, J. Cragun, R. Sutphen, Johnathan M. Lancaster

School of Medicine

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

Abstract

Ovarian cancer is the fifth leading cause of cancer death among women in the United States and western Europe. Platinum drugs are the most active agents in epithelial ovarian cancer therapy. In order to improve the prediction of response to platinum-based chemotherapy for advanced-stage ovarian cancers, we describe an integrated model which combines clinical information tumor and treatment information, with gene expression profile. This integrated modeling framework is based on the support vector machine classifier that evaluates the contributions of both clinical and gene expression data. The results show that the integrated model combining clinical information and gene expression profiles improve the prediction accuracy compared to those made by using gene expression predictor alone.

Original language	English (US)
Title of host publication	Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
Pages	4818-4821
Number of pages	4
State	Published - 2005
Event	2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005 - Shanghai, China Duration: Sep 1 2005 → Sep 4 2005

Publication series

Name	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume	7 VOLS
ISSN (Print)	0589-1019

Other

Other	2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
Country	China
City	Shanghai
Period	9/1/05 → 9/4/05

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

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Li, L., Chen, L., Goldgof, D., George, F., Chen, Z., Rao, A., Cragun, J., Sutphen, R., & Lancaster, J. M. (2005). Integration of clinical information and gene expression profiles for prediction of chemo-response for ovarian cancer. In Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005 (pp. 4818-4821). [1615550] (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings; Vol. 7 VOLS).

Integration of clinical information and gene expression profiles for prediction of chemo-response for ovarian cancer. / Li, Lihua; Chen, Li; Goldgof, D.; George, F.; Chen, Z.; Rao, A.; Cragun, J.; Sutphen, R.; Lancaster, Johnathan M.

Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005. 2005. p. 4818-4821 1615550 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings; Vol. 7 VOLS).

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

Li, L, Chen, L, Goldgof, D, George, F, Chen, Z, Rao, A, Cragun, J, Sutphen, R & Lancaster, JM 2005, Integration of clinical information and gene expression profiles for prediction of chemo-response for ovarian cancer. in Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005., 1615550, Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, vol. 7 VOLS, pp. 4818-4821, 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005, Shanghai, China, 9/1/05.

Li L, Chen L, Goldgof D, George F, Chen Z, Rao A et al. Integration of clinical information and gene expression profiles for prediction of chemo-response for ovarian cancer. In Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005. 2005. p. 4818-4821. 1615550. (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings).

Li, Lihua ; Chen, Li ; Goldgof, D. ; George, F. ; Chen, Z. ; Rao, A. ; Cragun, J. ; Sutphen, R. ; Lancaster, Johnathan M. / Integration of clinical information and gene expression profiles for prediction of chemo-response for ovarian cancer. Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005. 2005. pp. 4818-4821 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings).

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abstract = "Ovarian cancer is the fifth leading cause of cancer death among women in the United States and western Europe. Platinum drugs are the most active agents in epithelial ovarian cancer therapy. In order to improve the prediction of response to platinum-based chemotherapy for advanced-stage ovarian cancers, we describe an integrated model which combines clinical information tumor and treatment information, with gene expression profile. This integrated modeling framework is based on the support vector machine classifier that evaluates the contributions of both clinical and gene expression data. The results show that the integrated model combining clinical information and gene expression profiles improve the prediction accuracy compared to those made by using gene expression predictor alone.",

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AU - Li, Lihua

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AB - Ovarian cancer is the fifth leading cause of cancer death among women in the United States and western Europe. Platinum drugs are the most active agents in epithelial ovarian cancer therapy. In order to improve the prediction of response to platinum-based chemotherapy for advanced-stage ovarian cancers, we describe an integrated model which combines clinical information tumor and treatment information, with gene expression profile. This integrated modeling framework is based on the support vector machine classifier that evaluates the contributions of both clinical and gene expression data. The results show that the integrated model combining clinical information and gene expression profiles improve the prediction accuracy compared to those made by using gene expression predictor alone.

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