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 | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
| Pages | 4818-4821 |
| Number of pages | 4 |
| Volume | 7 VOLS |
| State | Published - 2005 |
| Externally published | Yes |
| 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 |
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 |
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ASJC Scopus subject areas
- Bioengineering
Cite this
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.
Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. Vol. 7 VOLS 2005. p. 4818-4821 1615550.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Integration of clinical information and gene expression profiles for prediction of chemo-response for ovarian cancer
AU - Li, Lihua
AU - Chen, Li
AU - Goldgof, D.
AU - George, F.
AU - Chen, Z.
AU - Rao, A.
AU - Cragun, J.
AU - Sutphen, R.
AU - Lancaster, Johnathan M.
PY - 2005
Y1 - 2005
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=33846907544&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33846907544&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:33846907544
SN - 0780387406
SN - 9780780387409
VL - 7 VOLS
SP - 4818
EP - 4821
BT - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
ER -