TY - GEN
T1 - Gene extraction and identification tumor/cancer for microarray data of ovarian cancer
AU - Lee, Zne Jung
AU - Lin, Shih Wei
AU - Hsu, Cheng Chic Veritas
AU - Huang, Yen Po
PY - 2006
Y1 - 2006
N2 - A typical microarray data of ovarian cancer consists of the expressions of tens of thousands of genes on a genomic scale. To avoid higher computational complexity, we want to find the most likely differentially expressed gene that best explain the effects of tumor/cancer for ovarian cancer. In this paper, we derive a hybrid approach for extracting and evaluating informative genes from microarray data of ovarian cancer. Furthermore, the expression patterns of extracted genes are used to identify tumor/cancer for ovarian cancer. In the proposed approach, the method analysis of variance (ANOVA) will, using P-value, test gene expressions that are significantly different in microarray data. The genetic algorithm is applied to extract genes and then the support vector machine is processed to identify tumor/cancer for ovarian cancer. We show that this extracted set of genes can be used to significantly identify ovarian tumors (OVT) and ovarian cancers (OVCA).
AB - A typical microarray data of ovarian cancer consists of the expressions of tens of thousands of genes on a genomic scale. To avoid higher computational complexity, we want to find the most likely differentially expressed gene that best explain the effects of tumor/cancer for ovarian cancer. In this paper, we derive a hybrid approach for extracting and evaluating informative genes from microarray data of ovarian cancer. Furthermore, the expression patterns of extracted genes are used to identify tumor/cancer for ovarian cancer. In the proposed approach, the method analysis of variance (ANOVA) will, using P-value, test gene expressions that are significantly different in microarray data. The genetic algorithm is applied to extract genes and then the support vector machine is processed to identify tumor/cancer for ovarian cancer. We show that this extracted set of genes can be used to significantly identify ovarian tumors (OVT) and ovarian cancers (OVCA).
UR - https://www.scopus.com/pages/publications/34547593765
U2 - 10.1109/TENCON.2006.343993
DO - 10.1109/TENCON.2006.343993
M3 - 会议稿件
AN - SCOPUS:34547593765
SN - 1424405491
SN - 9781424405497
T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON
BT - 2006 IEEE Region 10 Conference, TENCON 2006
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2006 IEEE Region 10 Conference, TENCON 2006
Y2 - 14 November 2006 through 17 November 2006
ER -
