A Decision Tree Based Classifier to Analyze XXXX Ovarian Cancer cDNA Microarray Datasets.

@entity43 @entity1 @entity5
@entity43 is the deadliest gynaecological disease because of the high mortality rate and there is no any symptom in @entity5 early stage. It was often the @entity5 period when @entity1 were diagnosed with @entity43 and thus delays a good opportunity of treatment. The current common method for detecting @entity43 is blood testing for analyzing the @entity5 marker CA-125 of serum. However, specificity and sensitivity of CA-125 are insufficient for early detection. Therefore, it has become an urgent issue to look for an efficient method which precisely detects the @entity5 markers for @entity43 . This study aims to find the target genes of @entity43 by different algorithms of information science. Feature selection and decision tree were applied to analyze 9600 @entity43 -related genes. After screening the target genes, candidate genes will be analyzed by Ingenuity Pathway Analysis (IPA) software to create a genetic pathway model and to understand the interactive relationship in the different pathological stages of @entity43 . Finally, this research found 9 oncogenes associated with @entity43 and some genes had not been discovered in previous studies. This system will assist medical staffs in diagnosis and treatment at @entity5 early stage and improve the @entity1 's survival.
