title: A bio-inspired computing model for ovarian carcinoma classification and oncogene detection.
abstract: BACKGROUND
  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.
  
  
  METHODS
  http://mht.mis.nchu.edu.tw/moodle/course/view.php?id=7.
authors.affiliation: Department of Management Information System and Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung City 402, Taiwan, Department of Information Management, National Taichung University of Science and Technology, Taichung City 404, Taiwan, Institute of Nanotechnology, National Chiao Tung University, Hsinchu City 300, Taiwan and Department of Obstetrics and Gynecology, China Medical University and Hospital, Taichung City 404, Taiwan Department of Management Information System and Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung City 402, Taiwan, Department of Information Management, National Taichung University of Science and Technology, Taichung City 404, Taiwan, Institute of Nanotechnology, National Chiao Tung University, Hsinchu City 300, Taiwan and Department of Obstetrics and Gynecology, China Medical University and Hospital, Taichung City 404, Taiwan.
authors.forename: Meng-Hsiun
authors.initials: MH
authors.lastname: Tsai
authors.affiliation: Department of Management Information System and Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung City 402, Taiwan, Department of Information Management, National Taichung University of Science and Technology, Taichung City 404, Taiwan, Institute of Nanotechnology, National Chiao Tung University, Hsinchu City 300, Taiwan and Department of Obstetrics and Gynecology, China Medical University and Hospital, Taichung City 404, Taiwan.
authors.forename: Mu-Yen
authors.initials: MY
authors.lastname: Chen
authors.affiliation: Department of Management Information System and Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung City 402, Taiwan, Department of Information Management, National Taichung University of Science and Technology, Taichung City 404, Taiwan, Institute of Nanotechnology, National Chiao Tung University, Hsinchu City 300, Taiwan and Department of Obstetrics and Gynecology, China Medical University and Hospital, Taichung City 404, Taiwan.
authors.forename: Steve G
authors.initials: SG
authors.lastname: Huang
authors.affiliation: Department of Management Information System and Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung City 402, Taiwan, Department of Information Management, National Taichung University of Science and Technology, Taichung City 404, Taiwan, Institute of Nanotechnology, National Chiao Tung University, Hsinchu City 300, Taiwan and Department of Obstetrics and Gynecology, China Medical University and Hospital, Taichung City 404, Taiwan.
authors.forename: Yao-Ching
authors.initials: YC
authors.lastname: Hung
authors.affiliation: Department of Management Information System and Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung City 402, Taiwan, Department of Information Management, National Taichung University of Science and Technology, Taichung City 404, Taiwan, Institute of Nanotechnology, National Chiao Tung University, Hsinchu City 300, Taiwan and Department of Obstetrics and Gynecology, China Medical University and Hospital, Taichung City 404, Taiwan.
authors.forename: Hsin-Chieh
authors.initials: HC
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics*; D003198:Computer Simulation; D016208:Databases, Factual; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D009367:Neoplasm Staging; D009857:Oncogenes / Q000235:genetics*; D010051:Ovarian Neoplasms / Q000145:classification* / Q000235:genetics* / Q000473:pathology; D060388:Support Vector Machine
chemical_list: D014408:Biomarkers, Tumor