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標題: Study of Bio-inspired Algorithms Applied to Human cDNA Microarray Dataset of Ovarian Cancer Target Genes
作者: 蔡宇紘
Yu-Hung Tsai
關鍵字: Ovarian cancer;Microarray;Bio-inspired algorithms;卵巢癌;微陣列;仿生演算法
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The incidence of ovarian cancer in gynecological cancer is not high, but the mortality rate is first. Currently used as a tumor marker CA-125 screening tool, but the lack of CA-125 accuracy. Therefore, to find a better screening tool for ovarian cancer is a very important issue. This study is mainly composed of microarray dataset in ovarian cancer patients, by data mining methods, establish a distinguished model of stages of ovarian cancer, and to identify the target genes. First, the ovarian cancer microarray dataset used in filter technology to pick out important genes, then important genes used in wrapper technology to pick out target genes. The main technology wrapper is the use of Bio-inspired Algorithms, including Back Propagation Artificial Neural Network, Genetic Algorithm, Particle Swarm Optimization, Artificial Fish Swarm Algorithm. The results of this study, this study found that the best use of stages of ovarian cancer classifier, whenever it detects seven target genes ITGB2, PLEC, POSTN, C1S, FN1, CDK5, PAPPA, you can distinguish between the four of stages of ovarian cancer, accuracy up to 97.16%.The results can be used as biological and medical important reference, after proven to be the future of biological experiments and medical experiments, expectations as ovarian cancer screening tool, allowing doctors, early detection and early treatment to improve survival in patients with ovarian cancer.

其他識別: U0005-0708201520081500
Rights: 同意授權瀏覽/列印電子全文服務,2018-08-11起公開。
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