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研究生:黃琮榆
研究生(外文):Tsung-Yu Huang
論文名稱:類神經網路於肝癌與卵巢癌質譜資料分類之應用
論文名稱(外文):Classification of Hepatocellular Carcinoma and Ovarian Cancer Spectrometry Samples Using Neural Network
指導教授:姚立德姚立德引用關係陳文輝陳文輝引用關係
口試委員:阮雪芬黃宣誠曾傳蘆
口試日期:2007-07-27
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:自動化科技研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:112
中文關鍵詞:類神經網路SELDI-TOF特徵向量前置處理
外文關鍵詞:Neural NetworkSELDI-TOFeigenvectordata preprocess
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近年來由於質譜技術的快速成長,使得蛋白質體學在生物醫學上對於癌症之診斷、檢驗等逐漸受到重視。但是,質譜技術在分類工作中往往受到資料維度過高與雜訊干擾所困擾,所以蛋白質體分析的前置處理在本文中是很重要的一環。本文主要探討的癌症是肝癌與卵巢癌,其原始資料皆由表面強化雷射解析電離飛行質譜技術 (surface enhance laser desorption/ionization time-of-flight mass spectrometry)所產生,其中肝癌的原始資料先經由Ciphergen ProteinChip Software分析,再加上本文提出ㄧ種特徵向量產生的方法處理後,能有效的解決維度過高與雜訊干擾的問題。由於卵巢癌的原始資料過於複雜,所以降低質譜的高維度並從中擷取出有意義的特徵峰值便成為本文另一個研究目標,特徵峰值選取的方法諸如峰點偵測、質譜校準。最後再將這些篩選過後的特徵峰值經由類神經網路來做分類,辨識效果皆可達到90%以上。未來之研究,可考慮結合其他特徵選取方式,更進一步縮減維度甚至提高分類準確度。
Recently, the mass spectrometry is developed with a fast rate. So the proteomics are applied to the classification and diagnosis of cancer has more respect. However, the classified proteomic data isn’t gotten easily, because of high dimension and noise. So, to analyze the data preprocess of proteomics is a very important part in this thesis. In this study, the cancer we discussed with are hepatocellular carcinoma and ovarian cancer. Their original samples were produced by surface enhance laser desorption/ionization time-of-flight mass spectrometry. And the original samples of hepatocellular carcinoma were analyzed by Ciphergen proteinChip Software first. And then, we could solve the problem, high dimension and noise, effectively by using the method that was proposed in this thesis to find the eigenvectors. Because the original samples of ovarian cancer are complex, the goal of this thesis is to reduce the high dimensionality of the mass spectrometry and to extract the significant peak-features for further study. The methods such as peak detection and spectra alignment are used for feature extraction. Finally, classifing the sifted significant peak-features by neural network and the rate of recognition can achieve ninety percent. In the future study, we could consider combining with other feature selection ways to reduce the number of dimensions and to increase the accuracy of classification.
中文摘要 i
英文摘要 ii
誌謝 iv
目錄 v
表目錄 vii
圖目錄 ix
第一章 緒論 1
1.1 前言 1
1.2 研究動機 3
1.3 研究目的 3
1.4 文獻探討 4
1.5 章節流程與說明 5
第二章 SELDI-TOF MS原始資料的分析 7
2.1 SELDI-TOF MS資料的研究 7
2.2 SELDI-TOF MS資料的特性 10
2.3 峰值資料的分析 11
2.4 研究數據來源 15
第三章 類神經網路與學習理論 17
3.1 類神經網路之定義 17
3.2 類神經網路的發展歷史 17
3.3 類神經網路的架構 19
3.4 類神經網路之分類 20
3.5 倒傳遞類神經網路 23
3.5.1倒傳遞類神經網路發展簡介 23
3.5.2倒傳遞類神經網路的基本結構 24
3.5.3倒傳遞類神經網路的運作過程 27
3.5.4倒傳遞類神經網路的參數 31
3.5.5倒傳遞類神經網路的優缺點 33

第四章 研究方法 34
4.1 前言 34
4.2 最大包絡峰值搜尋 36
4.3 三點中央搜尋 44
4.4 質譜峰位置調整 47
4.5 特徵選取 49
4.6 倒傳遞類神經網路 51
4.6.1改良後的倒傳遞類神經網路 51
4.6.2交互驗證法 54
第五章 實驗結果 55
5.1 前言 55
5.2 卵巢癌的實驗結果 55
5.2.1使用不同寬度參數的實驗結果 66
5.2.2使用變動寬度參數的實驗結果 75
5.3 肝癌的實驗結果 86
5.3.1一般的倒傳遞類神經網路 92
5.3.2改良後的倒傳遞類神經網路 100
第六章 結論與未來展望 105
6.1 結論 105
6.2 未來展望 105
參考文獻 107
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