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dc.contributor.authorTsai, Meng-Hsiunen_US
dc.contributor.authorChen, Mu-Yenen_US
dc.contributor.authorHuang, Steve G.en_US
dc.contributor.authorHung, Yao-Chingen_US
dc.contributor.authorWang, Hsin-Chiehen_US
dc.date.accessioned2015-07-21T08:29:23Z-
dc.date.available2015-07-21T08:29:23Z-
dc.date.issued2015-04-01en_US
dc.identifier.issn1367-4803en_US
dc.identifier.urihttp://dx.doi.org/10.1093/bioinformatics/btu782en_US
dc.identifier.urihttp://hdl.handle.net/11536/124461-
dc.description.abstractMotivation: Ovarian cancer is the fifth leading cause of cancer deaths in women in the western world for 2013. In ovarian cancer, benign tumors turn malignant, but the point of transition is difficult to predict and diagnose. The 5-year survival rate of all types of ovarian cancer is 44%, but this can be improved to 92% if the cancer is found and treated before it spreads beyond the ovary. However, only 15% of all ovarian cancers are found at this early stage. Therefore, the ability to automatically identify and diagnose ovarian cancer precisely and efficiently as the tissue changes from benign to invasive is important for clinical treatment and for increasing the cure rate. This study proposes a new ovarian carcinoma classification model using two algorithms: a novel discretization of food sources for an artificial bee colony (DfABC), and a support vector machine (SVM). For the first time in the literature, oncogene detection using this method is also investigated. Results: A novel bio-inspired computing model and hybrid algorithms combining DfABC and SVM was applied to ovarian carcinoma and oncogene classification. This study used the human ovarian cDNA expression database to collect 41 patient samples and 9600 genes in each pathological stage. Feature selection methods were used to detect and extract 15 notable oncogenes. We then used the DfABC-SVM model to examine these 15 oncogenes, dividing them into eight different classifications according to their gene expressions of various pathological stages. The average accuracyof the eight classification experiments was 94.76%. This research also found some oncogenes that had not been discovered or indicated in previous scientific studies. The main contribution of this research is the proof that these newly discovered oncogenes are highly related to ovarian or other cancers.en_US
dc.language.isoen_USen_US
dc.titleA bio-inspired computing model for ovarian carcinoma classification and oncogene detectionen_US
dc.typeArticleen_US
dc.identifier.doi10.1093/bioinformatics/btu782en_US
dc.identifier.journalBIOINFORMATICSen_US
dc.citation.volume31en_US
dc.citation.spage1102en_US
dc.citation.epage1110en_US
dc.contributor.department材料科學與工程學系奈米科技碩博班zh_TW
dc.contributor.departmentGraduate Program of Nanotechnology , Department of Materials Science and Engineeringen_US
dc.identifier.wosnumberWOS:000352269500016en_US
dc.citation.woscount0en_US
Appears in Collections:Articles