TY - GEN
T1 - Improving reliability of response prediction to platinum-based therapy by AdaBoost and multiple classifiers
AU - Chen, Li
AU - Li, Lihua
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 - It is a challenge to construct a reliable classifier based on microarray gene expression data for prediction of chemotherapy response, because usually only a small number of samples are available and each sample has thousands of gene expressions. This paper uses boosting and bootstrap approaches to improve the reliability of prediction. Specifically, AdaBoost and multiple classifiers based methods are used, in which support vector machines (SVMs) are utilized as the classifiers due to their good generalization ability. We compare the performance of proposed methods with a single SVM classifier system using MAS gene expression dataset in prediction of the response to platinum-based therapy for advanced-stage ovarian cancers. Statistical tests show both of the proposed methods achieve better prediction performance and have good reliability in terms of mean and standard deviation of the prediction performance for different number of selected features.
AB - It is a challenge to construct a reliable classifier based on microarray gene expression data for prediction of chemotherapy response, because usually only a small number of samples are available and each sample has thousands of gene expressions. This paper uses boosting and bootstrap approaches to improve the reliability of prediction. Specifically, AdaBoost and multiple classifiers based methods are used, in which support vector machines (SVMs) are utilized as the classifiers due to their good generalization ability. We compare the performance of proposed methods with a single SVM classifier system using MAS gene expression dataset in prediction of the response to platinum-based therapy for advanced-stage ovarian cancers. Statistical tests show both of the proposed methods achieve better prediction performance and have good reliability in terms of mean and standard deviation of the prediction performance for different number of selected features.
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M3 - Conference contribution
AN - SCOPUS:33846912988
SN - 0780387406
SN - 9780780387409
T3 - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
SP - 4822
EP - 4825
BT - Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
T2 - 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
Y2 - 1 September 2005 through 4 September 2005
ER -