博碩士論文 93522059 詳細資訊




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姓名 陳明壽(Ming-Shou Chen)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 利用統計方法尋找質譜圖上侯選生物指標
(Biomarker discovery on high-throughput and high-resolution mass spectrometry oral cancer data using statistical methods)
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摘要(中) 由於末期癌症的死亡率相當高,所以需要能在初期就將癌症給診斷出來的技術及方法,近年來在質譜技術上的進步使得該診斷的可能性大大的增加,因此高解析度質譜圖資料的分析逐漸被應用在疾病分類及診斷指導。質譜儀器在解析度及校正上的誤差使得質譜圖資料產生質量峰偏移的現像(peak shifting),而一般來說質譜圖上的質量峰(peak)又高達幾十萬個,這兩個問題使得分析質譜圖時產生困難而且要花費大量的時間,我們提出以區間最大值的方式來處理質量峰偏移現像並利用統計分析快速的在幾十萬個質量峰中找到較少量且具辨別力的質量峰(biomarker candidates)。最後應用所提出之方法在長庚大學蛋白質體質心實驗室所提供的口腔癌上做測試。
摘要(英) Due to the high death rate in advanced stage diseases, the diagnosis of early-stage cancer is needed for public health. Recent advances in the biotechnology of high-throughput and high-resolution MALDI-TOF mass spectrometry (MS) has made such diagnosis possible (Petricoin and Liotta 2003). From then on, high-resolution mass spectrometers are increasingly used for disease classification and therapeutic guidance. Due to instrument resolution and/or instrument calibration, the mass spectrometry (MS) data may be poor in quality. The problem makes it difficult and time consuming to trace each spectrum with thousands of features for biomarkers. We proposed a region-based alignment method to deal with peak shifting problem and applied statistical method to select most discriminatory peaks as biomarker candidates. Finally, we test our methodology on the oral cancer dataset from CHANG GUNG UNIVERSITY in Taiwan.
關鍵字(中) ★ 生物指標
★ 質譜圖
★ 統計分析
關鍵字(英) ★ biomarker discovery
★ peak alignment
★ mass spectrometry
論文目次 Table of content III
List of Figures IV
List of Tables VI
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 3
1.3 Goal 3
Chapter 2 Related Works 5
2.1 Mass spectrometry 5
2.2 SpecAlign 6
2.3 Weka 7
2.4 Analysis pipelines proposed before 8
Chapter 3 Materials and Methods 10
3.1 Materials 10
3.2 Methods 12
3.2.1 Data preprocessing 12
3.2.2 Feature selection 15
3.2.3 Classification 17
Chapter 4 Results 19
Chapter 5 Discussion 33
Reference 34
Appendix 35
參考文獻 Adam, B. L., Y. Qu, et al. (2002). "Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men." Cancer Res 62(13): 3609-14.
Baggerly, K. A., J. S. Morris, et al. (2004). "Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments." Bioinformatics 20(5): 777-85.
Cheng, A. J., L. C. Chen, et al. (2005). "Oral cancer plasma tumor marker identified with bead-based affinity-fractionated proteomic technology." Clin Chem 51(12): 2236-44.
Conrads, T. P., V. A. Fusaro, et al. (2004). "High-resolution serum proteomic features for ovarian cancer detection." Endocr Relat Cancer 11(2): 163-78.
Diamandis, E. P. (2002). "Proteomic patterns in serum and identification of ovarian cancer." Lancet 360(9327): 170; author reply 170-1.
Foundation, O. C.
Geurts, P., M. Fillet, et al. (2005). "Proteomic mass spectra classification using decision tree based ensemble methods." Bioinformatics 21(14): 3138-45.
Kamber, J. H. a. M. (2000). "Data Mining: Concepts and Techniques."
Li, J., Z. Zhang, et al. (2002). "Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer." Clin Chem 48(8): 1296-304.
Petricoin, E. F., A. M. Ardekani, et al. (2002). "Use of proteomic patterns in serum to identify ovarian cancer." Lancet 359(9306): 572-7.
Petricoin, E. F. and L. A. Liotta (2003). "Mass spectrometry-based diagnostics: the upcoming revolution in disease detection." Clin Chem 49(4): 533-4.
Qu, Y., B. L. Adam, et al. (2002). "Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients." Clin Chem 48(10): 1835-43.
Semmes, O. J., Z. Feng, et al. (2005). "Evaluation of serum protein profiling by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry for the detection of prostate cancer: I. Assessment of platform reproducibility." Clin Chem 51(1): 102-12.
Vlahou, A., J. O. Schorge, et al. (2003). "Diagnosis of Ovarian Cancer Using Decision Tree Classification of Mass Spectral Data." J Biomed Biotechnol 2003(5): 308-314.
Wong, J. W., G. Cagney, et al. (2005). "SpecAlign--processing and alignment of mass spectra datasets." Bioinformatics 21(9): 2088-90.
Yu, J. and X. W. Chen (2005). "Bayesian neural network approaches to ovarian cancer identification from high-resolution mass spectrometry data." Bioinformatics 21 Suppl 1: i487-94.
Yu, J. S., S. Ongarello, et al. (2005). "Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data." Bioinformatics 21(10): 2200-9.
指導教授 洪炯宗、陳廣典
(Jorng-Tzong Horng、Kuang-Den Chen)
審核日期 2006-7-18
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