MOTIVATION: 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. AVAILABILITY AND IMPLEMENTATION: http://mht.mis.nchu.edu.tw/moodle/course/view.php?id=7.

Microarray experiments were performed to normal ovaries, benign ovarian tumor and stage I and III of ovarian cancer. By treating benign ovarian tumor as pre-cancerous stage and comparing to later stages of ovarian cancer we identified 46 genes overly expressed in early-staged cancer. We also identified over-expressed genes at all stages of ovarian tumor/cancer. The over-expressed genes might be alternative candidates for developing early diagnostic markers. JUN and nuclear receptor subfamily genes were repeatedly identified in the group of down-regulated genes indicating their important regulatory roles in the oncogenesis of ovary.