NDLI: A Decision Tree Based Classifier to Analyze Human Ovarian Cancer cDNA Microarray Datasets

Content Provider	Springer Nature Link
Author	Tsai, Meng Hsiun Wang, Hsin Chieh Lee, Guan Wei Lin, Yi Chen Chiu, Sheng Hsiung
Copyright Year	2015
Abstract	Ovarian cancer is the deadliest gynaecological disease because of the high mortality rate and there is no any symptom in cancer early stage. It was often the terminal cancer period when patients were diagnosed with ovarian cancer and thus delays a good opportunity of treatment. The current common method for detecting ovarian cancer is blood testing for analyzing the tumor 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 tumor markers for ovarian cancer. This study aims to find the target genes of ovarian cancer by different algorithms of information science. Feature selection and decision tree were applied to analyze 9600 ovarian cancer-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 ovarian cancer. Finally, this research found 9 oncogenes associated with ovarian cancer and some genes had not been discovered in previous studies. This system will assist medical staffs in diagnosis and treatment at cancer early stage and improve the patient’s survival.
Starting Page	1
Ending Page	8
Page Count	8
File Format	PDF
ISSN	01485598
Journal	Journal of Medical Systems
Volume Number	40
Issue Number	1
e-ISSN	1573689X
Language	English
Publisher	Springer US
Publisher Date	2015-11-03
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Ovarian cancer Decision tree C&RT CHAID Ingenuity Pathway Analysis Health Informatics Statistics for Life Sciences, Medicine, Health Sciences
Content Type	Text
Resource Type	Article
Subject	Health Information Management Information Systems Health Informatics

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