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題名 兩階段特徵選取法在蛋白質質譜儀資料之應用
兩階段特徵選取法在蛋白質質譜儀資料之應用
A Two-Stage Approach of Feature Selection on Proteomic Spectra Data
A Two-Stage Approach of Feature Selection on Proteomic Spectra Data
作者 王健源
Wang,Chien-yuan
貢獻者 張源俊<br>郭訓志
王健源
Wang,Chien-yuan
關鍵詞 特徵選取
特徵選取
基因演算法
基因演算法
表面增強雷射脫附游離/飛行時間質譜
表面增強雷射脫附游離/飛行時間質譜
支援向量機
支援向量機
Feature Selection
Feature Selection
Genetic Algorithm (GA)
Genetic Algorithm (GA)
SELDI
SELDI
Support Vector Machines (SVM)
Support Vector Machines (SVM)
日期 2005
上傳時間 2009-09-14
摘要 藉由「早期發現，早期治療」的方式，我們可以降低癌症的死亡率。因此找出與癌症病變有關的生物標記以期及早發現與治療是一項重要的工作。本研究分析了包含正常人以及攝護腺癌症病人實際的蛋白質質譜資料，而這些蛋白質質譜資料是來自於表面強化雷射解吸電離飛行質譜技術（SELDI-TOF MS）的蛋白質晶片實驗。表面增強雷射脫附遊離飛行時間質譜技術可有效地留存生物樣本的蛋白質特徵。如果沒有經過適當的事前處理步驟以消除實驗雜訊，ㄧ 個質譜中可能包含多於數百或數千的特徵變數。為了加速對於可能的蛋白質生物標記的搜尋，我們只考慮可以區分癌症病人與正常人的特徵變數。
     基因演算法是一種類似生物基因演化的總體最佳化搜尋機制，它可以有效地在高維度空間中去尋找可能的最佳解。本研究中，我們利用仿基因演算法(GAL)進行蛋白質的特徵選取以區分癌症病人與正常人。另外，我們提出兩種兩階段仿基因演算法(TSGAL)，以嘗試改善仿基因演算法的缺點。
藉由「早期發現，早期治療」的方式，我們可以降低癌症的死亡率。因此找出與癌症病變有關的生物標記以期及早發現與治療是一項重要的工作。本研究分析了包含正常人以及攝護腺癌症病人實際的蛋白質質譜資料，而這些蛋白質質譜資料是來自於表面強化雷射解吸電離飛行質譜技術（SELDI-TOF MS）的蛋白質晶片實驗。表面增強雷射脫附遊離飛行時間質譜技術可有效地留存生物樣本的蛋白質特徵。如果沒有經過適當的事前處理步驟以消除實驗雜訊，ㄧ 個質譜中可能包含多於數百或數千的特徵變數。為了加速對於可能的蛋白質生物標記的搜尋，我們只考慮可以區分癌症病人與正常人的特徵變數。
     基因演算法是一種類似生物基因演化的總體最佳化搜尋機制，它可以有效地在高維度空間中去尋找可能的最佳解。本研究中，我們利用仿基因演算法(GAL)進行蛋白質的特徵選取以區分癌症病人與正常人。另外，我們提出兩種兩階段仿基因演算法(TSGAL)，以嘗試改善仿基因演算法的缺點。
Early detection and diagnosis can effectively reduce the mortality of cancer. The discovery of biomarkers for the early detection and diagnosis of cancer is thus an important task. In this study, a real proteomic spectra data set of prostate cancer patients and normal patients was analyzed. The data were collected from a Surface-Enhanced Laser Desorption/Ionization Time-Of-Flight Mass Spectrometry (SELDI-TOF MS) experiment. The SELDI-TOF MS technology captures protein features in a biological sample. Without suitable pre-processing steps to remove experimental noise, a mass spectrum could consists of more than hundreds or thousands of peaks. To narrow down the search for possible protein biomarkers, only those features that can distinguish between cancer and normal patients are selected.
     Genetic Algorithm (GA) is a global optimization procedure that uses an analogy of the genetic evolution of biological organisms. It’s shown that GA is effective in searching complex high-dimensional space. In this study, we consider GA-Like algorithm (GAL) for feature selection on proteomic spectra data in classifying prostate cancer patients from normal patients. In addition, we propose two types of Two-Stage GAL algorithm (TSGAL) to improve the GAL.
Early detection and diagnosis can effectively reduce the mortality of cancer. The discovery of biomarkers for the early detection and diagnosis of cancer is thus an important task. In this study, a real proteomic spectra data set of prostate cancer patients and normal patients was analyzed. The data were collected from a Surface-Enhanced Laser Desorption/Ionization Time-Of-Flight Mass Spectrometry (SELDI-TOF MS) experiment. The SELDI-TOF MS technology captures protein features in a biological sample. Without suitable pre-processing steps to remove experimental noise, a mass spectrum could consists of more than hundreds or thousands of peaks. To narrow down the search for possible protein biomarkers, only those features that can distinguish between cancer and normal patients are selected.
     Genetic Algorithm (GA) is a global optimization procedure that uses an analogy of the genetic evolution of biological organisms. It’s shown that GA is effective in searching complex high-dimensional space. In this study, we consider GA-Like algorithm (GAL) for feature selection on proteomic spectra data in classifying prostate cancer patients from normal patients. In addition, we propose two types of Two-Stage GAL algorithm (TSGAL) to improve the GAL.
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Gretzer, M.B., Chan, D.W., van Rootselaar, C.L., Rosenzweig, J.M., Dalrymple, S., Mangold, L.A., Partin, A.W., and Veltri, R.W.(2004). Proteomic Analysis of Dunning Prostate Cancer Cell Lines With Variable Metastatic Potential Using SELDI-TOF. Prostate (60), 325-331.
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Lilien, R., Farid, H., and Donald, B.(2003). Probabilistic Disease Classification of Expression-Dependent Proteomic Data from Mass Spectrometry of Human Serum. Journal of Computational Biology 10(6):925-946.
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Petricoin, E.F., Ardekani, A.M., Hitt, B.A., Levine, P.J., Fusaro, V.A., Steinberg, S.M., Mills, G.B., Simone, C., Fishman, D.A., Kohn, E.C., and Liotta, L.A.(2002a). Use of Proteomic Patterns in Serum to Identify Ovarian Cancer. Lancet 359(9306), 572–577.
Petricoin, E.F., Ardekani, A.M., Hitt, B.A., Levine, P.J., Fusaro, V.A., Steinberg, S.M., Mills, G.B., Simone, C., Fishman, D.A., Kohn, E.C., and Liotta, L.A.(2002a). Use of Proteomic Patterns in Serum to Identify Ovarian Cancer. Lancet 359(9306), 572–577.
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描述 碩士
碩士
國立政治大學
國立政治大學
統計研究所
統計研究所
93354025
93354025
94
94
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0933540252
http://thesis.lib.nccu.edu.tw/record/#G0933540252
資料類型 thesis
thesis
dc.contributor.advisor 張源俊<br>郭訓志zh_TW
dc.contributor.author (Authors) 王健源zh_TW
dc.contributor.author (Authors) Wang,Chien-yuanen_US
dc.creator (作者) 王健源zh_TW
dc.creator (作者) Wang,Chien-yuanen_US
dc.date (日期) 2005en_US
dc.date.accessioned 2009-09-14-
dc.date.available 2009-09-14-
dc.date.issued (上傳時間) 2009-09-14-
dc.identifier (Other Identifiers) G0933540252en_US
dc.identifier (Other Identifiers) G0933540252en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/30954-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/30954-
dc.description (描述) 碩士zh_TW
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 統計研究所zh_TW
dc.description (描述) 統計研究所zh_TW
dc.description (描述) 93354025zh_TW
dc.description (描述) 93354025zh_TW
dc.description (描述) 94zh_TW
dc.description (描述) 94zh_TW
dc.description.abstract (摘要) 藉由「早期發現，早期治療」的方式，我們可以降低癌症的死亡率。因此找出與癌症病變有關的生物標記以期及早發現與治療是一項重要的工作。本研究分析了包含正常人以及攝護腺癌症病人實際的蛋白質質譜資料，而這些蛋白質質譜資料是來自於表面強化雷射解吸電離飛行質譜技術（SELDI-TOF MS）的蛋白質晶片實驗。表面增強雷射脫附遊離飛行時間質譜技術可有效地留存生物樣本的蛋白質特徵。如果沒有經過適當的事前處理步驟以消除實驗雜訊，ㄧ 個質譜中可能包含多於數百或數千的特徵變數。為了加速對於可能的蛋白質生物標記的搜尋，我們只考慮可以區分癌症病人與正常人的特徵變數。
     基因演算法是一種類似生物基因演化的總體最佳化搜尋機制，它可以有效地在高維度空間中去尋找可能的最佳解。本研究中，我們利用仿基因演算法(GAL)進行蛋白質的特徵選取以區分癌症病人與正常人。另外，我們提出兩種兩階段仿基因演算法(TSGAL)，以嘗試改善仿基因演算法的缺點。		zh_TW
dc.description.abstract (摘要) 藉由「早期發現，早期治療」的方式，我們可以降低癌症的死亡率。因此找出與癌症病變有關的生物標記以期及早發現與治療是一項重要的工作。本研究分析了包含正常人以及攝護腺癌症病人實際的蛋白質質譜資料，而這些蛋白質質譜資料是來自於表面強化雷射解吸電離飛行質譜技術（SELDI-TOF MS）的蛋白質晶片實驗。表面增強雷射脫附遊離飛行時間質譜技術可有效地留存生物樣本的蛋白質特徵。如果沒有經過適當的事前處理步驟以消除實驗雜訊，ㄧ 個質譜中可能包含多於數百或數千的特徵變數。為了加速對於可能的蛋白質生物標記的搜尋，我們只考慮可以區分癌症病人與正常人的特徵變數。
     基因演算法是一種類似生物基因演化的總體最佳化搜尋機制，它可以有效地在高維度空間中去尋找可能的最佳解。本研究中，我們利用仿基因演算法(GAL)進行蛋白質的特徵選取以區分癌症病人與正常人。另外，我們提出兩種兩階段仿基因演算法(TSGAL)，以嘗試改善仿基因演算法的缺點。		zh_TW
dc.description.abstract (摘要) Early detection and diagnosis can effectively reduce the mortality of cancer. The discovery of biomarkers for the early detection and diagnosis of cancer is thus an important task. In this study, a real proteomic spectra data set of prostate cancer patients and normal patients was analyzed. The data were collected from a Surface-Enhanced Laser Desorption/Ionization Time-Of-Flight Mass Spectrometry (SELDI-TOF MS) experiment. The SELDI-TOF MS technology captures protein features in a biological sample. Without suitable pre-processing steps to remove experimental noise, a mass spectrum could consists of more than hundreds or thousands of peaks. To narrow down the search for possible protein biomarkers, only those features that can distinguish between cancer and normal patients are selected.
     Genetic Algorithm (GA) is a global optimization procedure that uses an analogy of the genetic evolution of biological organisms. It’s shown that GA is effective in searching complex high-dimensional space. In this study, we consider GA-Like algorithm (GAL) for feature selection on proteomic spectra data in classifying prostate cancer patients from normal patients. In addition, we propose two types of Two-Stage GAL algorithm (TSGAL) to improve the GAL.		en_US
dc.description.abstract (摘要) Early detection and diagnosis can effectively reduce the mortality of cancer. The discovery of biomarkers for the early detection and diagnosis of cancer is thus an important task. In this study, a real proteomic spectra data set of prostate cancer patients and normal patients was analyzed. The data were collected from a Surface-Enhanced Laser Desorption/Ionization Time-Of-Flight Mass Spectrometry (SELDI-TOF MS) experiment. The SELDI-TOF MS technology captures protein features in a biological sample. Without suitable pre-processing steps to remove experimental noise, a mass spectrum could consists of more than hundreds or thousands of peaks. To narrow down the search for possible protein biomarkers, only those features that can distinguish between cancer and normal patients are selected.
     Genetic Algorithm (GA) is a global optimization procedure that uses an analogy of the genetic evolution of biological organisms. It’s shown that GA is effective in searching complex high-dimensional space. In this study, we consider GA-Like algorithm (GAL) for feature selection on proteomic spectra data in classifying prostate cancer patients from normal patients. In addition, we propose two types of Two-Stage GAL algorithm (TSGAL) to improve the GAL.		en_US
dc.description.tableofcontents 1 Introduction 1
     2 Literature Review 3
     3 Descriptions of Data 9
     3.1 SELDI 9
     3.2 SELDI-TOF MS spectra of the prostate cancer 10
     3.3 Preprocessing of the Raw Spectra 12
     3.3.1 Baseline Subtraction 12
     3.3.2 Normalization 13
     3.3.3 Peak detection 13
     3.3.4 Peak alignment 13
     4 Methodologies 18
     4.1 SVM Classifier 19
     4.2 Genetic Algorithm (GA) 20
     4.2.1 Chromosome 23
     4.2.2 Fitness Function 23
     4.2.3 GA operators 23
     4.2.4 Termination 25
     4.3 GA-Like algorithm (GAL) 26
     4.4 Two-stage GAL algorithm (TSGAL) 26
     5 Data Analysis 30
     5.1 GA-Like for Feature Selection 30
     5.2 TSGAL for Feature Selection 35
     5.3 Comparisons between GAL and TSGALs 38
     6 Results and Discussion 40
     Reference 43
     Appendices 46		zh_TW
dc.description.tableofcontents 1 Introduction 1
     2 Literature Review 3
     3 Descriptions of Data 9
     3.1 SELDI 9
     3.2 SELDI-TOF MS spectra of the prostate cancer 10
     3.3 Preprocessing of the Raw Spectra 12
     3.3.1 Baseline Subtraction 12
     3.3.2 Normalization 13
     3.3.3 Peak detection 13
     3.3.4 Peak alignment 13
     4 Methodologies 18
     4.1 SVM Classifier 19
     4.2 Genetic Algorithm (GA) 20
     4.2.1 Chromosome 23
     4.2.2 Fitness Function 23
     4.2.3 GA operators 23
     4.2.4 Termination 25
     4.3 GA-Like algorithm (GAL) 26
     4.4 Two-stage GAL algorithm (TSGAL) 26
     5 Data Analysis 30
     5.1 GA-Like for Feature Selection 30
     5.2 TSGAL for Feature Selection 35
     5.3 Comparisons between GAL and TSGALs 38
     6 Results and Discussion 40
     Reference 43
     Appendices 46		zh_TW
dc.language.iso en_US-
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0933540252en_US
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0933540252en_US
dc.subject (關鍵詞) 特徵選取zh_TW
dc.subject (關鍵詞) 特徵選取zh_TW
dc.subject (關鍵詞) 基因演算法zh_TW
dc.subject (關鍵詞) 基因演算法zh_TW
dc.subject (關鍵詞) 表面增強雷射脫附游離/飛行時間質譜zh_TW
dc.subject (關鍵詞) 表面增強雷射脫附游離/飛行時間質譜zh_TW
dc.subject (關鍵詞) 支援向量機zh_TW
dc.subject (關鍵詞) 支援向量機zh_TW
dc.subject (關鍵詞) Feature Selectionen_US
dc.subject (關鍵詞) Feature Selectionen_US
dc.subject (關鍵詞) Genetic Algorithm (GA)en_US
dc.subject (關鍵詞) Genetic Algorithm (GA)en_US
dc.subject (關鍵詞) SELDIen_US
dc.subject (關鍵詞) SELDIen_US
dc.subject (關鍵詞) Support Vector Machines (SVM)en_US
dc.subject (關鍵詞) Support Vector Machines (SVM)en_US
dc.title (題名) 兩階段特徵選取法在蛋白質質譜儀資料之應用zh_TW
dc.title (題名) 兩階段特徵選取法在蛋白質質譜儀資料之應用zh_TW
dc.title (題名) A Two-Stage Approach of Feature Selection on Proteomic Spectra Dataen_US
dc.title (題名) A Two-Stage Approach of Feature Selection on Proteomic Spectra Dataen_US
dc.type (資料類型) thesisen
dc.type (資料類型) thesisen
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