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標題: 植基於資料探勘演算法在人類大腸癌資料集標靶基因篩選
Target Genes Selection for Human Colon Cancer Datasets Based on Data Mining Algorithm
作者: 徐仁徽
Syu, Jen-Hui
Contributors: 蔡孟勳
Meng-Hsiun Tsai
關鍵字: 大腸癌;倒傳遞類神經網路;隨機樹
Colon Cancer;Back-Propagation Neural Network;Random Tree
日期: 2013
Issue Date: 2013-11-19 12:02:38 (UTC+8)
Publisher: 基因體暨生物資訊學研究所
摘要: 行政院衛生福利部公告之101年十大死因統計,惡性腫瘤連續31年蟬聯首位。其中,因結腸、直腸和肛門癌死亡人數高達5131人,100年則為4921人,而大腸癌更是我國罹癌人數最多之癌症,且一直排名於癌症死因之中的第3名。有鑑於大腸癌若是能早期治療其成效差異非常大,如果患者能在早期發現大腸癌,90%的患者能在診斷後可再多活五年。大腸癌的發生隨著年齡增長,其影響因子也隨之增加,如飲食習慣、性別、家庭史或遺傳因素等,都可能造成大腸癌的發生。近來來,罹癌年齡層有逐年下降之趨勢,故有其探討之必要性。本論文開發建置了標靶基因資料庫及醫學資料探勘預測分析系統,利用標靶基因建置資料庫,整合類神經網路及決策樹等剖析技術,進一步探討大腸癌病患之標靶基因的影響因子。期望建立之輔助診斷系統,能讓病人受檢時降低因誤判所造成之延誤就醫的可能性,並節省高昂的醫療成本。研究結果顯示,以倒傳遞類神經網路分析準確率達62%,隨機樹分析準確率達95.6%。最後,本論文建置一套視覺化網頁介面系統,期望能讓醫療人員在診斷過程中更直觀且快速的觀察基因表現量,醫學研究融合資訊技術之方式,目的是希望將來能對大腸癌之治療與研究帶來效益。
In 2013, the ministry of health and welfare announcement ten leading causes of death. Malignancies is the first of the top ten causes of deaths for 31 years running. Because of the colon cancer, rectum cancer and anal cancer deaths have 5131 people. Colon cancer is the first most commonly diagnosed cancer in the Taiwan, and colon cancer is the third most common type of cancer in both sexes. When recognized at this early stage, they are often reversible. If the patients can early detection of colon cancer, life expectancy would lengthen if colon cancer risks are tackled. The risk of developing colon cancer increases with advancing age. Many risk Factors that increases the chance of getting a colon cancer. For example dietary habit, gender, family history and hereditary factors etc. Many factors might give rise to colon cancer risk. In the last few years, the average age of patient is lowering. This study have developed a novel system target genes, prediction analysis and medical data mining system. Which the use of target genes to build a database, integration of artificial neural networks and decision tree analysis techniques. Further Studies in colon cancer patients of target genes. Expect to establish the diagnostic system, reduce the misdiagnosis rate and reduce health care costs. Our experimental result show that the accuracy rate of back-propagation neural network and random tree are 62% and 95.6%. Finally, this thesis is to build a visual web interface systems, hoping to make medical staff in the diagnostic process more intuitive and fast observed gene expression. This study hope genes problem solving and medical decision making, motivated by efforts to improve human health.
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