利用赫伯特-黃轉換法改進在質譜儀分析技術上生物標記的偵測

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：60

、訪客IP：18.191.192.109

姓名

謝秉恆(Ping-heng Hsieh) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

利用赫伯特-黃轉換法改進在質譜儀分析技術上生物標記的偵測
(Improved candidate biomarker detection on mass spectrometry data using Hilbert Huang transform)

相關論文

★ 應用嵌入式系統於呼吸肌肉群訓練儀之系統開發	★ 勃起障礙與缺血性心臟病的雙向研究: 以台灣全人口基礎的世代研究
★ 基質輔助雷射脫附飛行時間式串聯質譜儀微生物抗藥性資料視覺化工具	★ 使用穿戴式裝置分析心律變異及偵測心律不整之應用程式
★ 建立一個自動化分析系統用來分析任何兩種疾病之間的關聯性透過世代研究設計以及使用承保抽樣歸人檔	★ 青光眼病患併發糖尿病,使用Metformin及Sulfonylurea治療得到中風之風險:以台灣人口為基礎的觀察性研究
★ 利用組成識別和序列及空間特性構成之預測系統來針對蛋白質交互作用上的特殊區段點位進行分析及預測辨識	★ 新聞語意特徵擷取流程設計與股價變化關聯性分析
★ 藥物與疾病關聯性自動化分析平台設計與實作	★ 建立財務報告自動分析系統進行股價預測
★ 建立一個分析疾病與癌症關聯性的自動化系統	★ 基於慣性感測器虛擬鍵盤之設計與實作
★ 一個醫療照護監測系統之實作	★ 應用手機開發手握球握力及相關資料之量測
★ 利用關聯分析全面性的搜索癌症關聯疾病	★ 全面性尋找類風濕性關節炎之關聯疾病

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

質譜儀技術現在已經被廣泛的運用在各個領域之中。在生物蛋白質的研究中，質譜儀技術主要是用來尋找生物標記。生物標記可以幫助醫生對於疾病的即時診斷，避免錯過治療的黃金時期。另外，生物標記也可用來開發新型藥物或診斷試劑。基質輔助雷射脫附游離法飛行質譜(MALDI-TOF)與表面強化雷射解析電離飛行質譜(SELDI-TOF)技術為常用來分析生物樣本的兩種質譜儀技術。我們發展了一套可用來分析MALDI-TOF與SELDI-TOF質譜圖的計算方法，我們稱之為E-HHTMass。E-HHTMass整合了質譜圖前處理方法與分類法，目的是為了能夠找出重要的波峰，而這些波峰有可能就是我們想尋找的生物標記。在前處理方法中，我們以赫伯特-黃轉換法(Hilbert-Huang Transform)為主要概念。研究結果顯示，我們的方法在處理MALDI-TOF與SELDI-TOF質譜圖方面有不錯的成效，尤其是在質譜圖中荷值比高頻率的區域。

摘要(英)

Mass spectrometry technique is now in widespread use. In biological proteome research, the mass spectrometry is mainly used for biomarker discovery. Biomarker discovery can help us to diagnose patients’’ condition in time, develop new medicines or diagnostic kits, and realize the characteristics of new diseases. MALDI-TOF (matrix assisted laser desorption/ionization time-of-flight) and SELDI-TOF (surface enhanced laser desorption/ionization time-of-flight) mass spectrometers are two usually used techniques for analyzing biological sample. We develop a novel computational method, called Enhance HHTMass (E-HHTMass), for dealing with the MALDI-TOF and SELDI-TOF mass spectrometry data. E-HHTMass integrates pre-processing and classification functions which have ability of reappearance significant peaks across mass spectra. We use the Hilbert Huang Transform in pre-processing mainly for purpose of denoising. The results show that E-HHTMass is good at dealing with MALDI-TOF and SELDI-TOF mass spectrum data, especially in high frequency area of m/z value of the spectrum.

關鍵字(中)

★ 生物標記
★ 質譜圖分析

關鍵字(英)

★ SELDI-TOF MS
★ MALDI-TOF MS
★ biomarker

論文目次

摘要 ........................................................................................................................ III
ABSTRACT ........................................................................................................... IV
致謝 ..........................................................................................................................V
CHAPTER 1 INTRODUCTION ......................................................................... 1
1.1 BACKGROUND ............................................................................................ 1
1.2 MOTIVATION ............................................................................................... 2
1.3 GOAL ......................................................................................................... 2
CHAPTER 2 RELATED WORKS ...................................................................... 3
2.1 RELATED TOOLS .......................................................................................... 3
2.2 RECENT TOOLS OF DISCOVERING BIOMARKER BY USING SELDI-TOF AND MALDI-TOF MS .................................................................................................. 4
CHAPTER 3 MATERIALS AND METHOD ...................................................... 8
3.1 DATA SOURCE ............................................................................................. 8
3.2 METHODS ................................................................................................... 9
CHAPTER 4 RESULTS ..................................................................................... 16
4.1 RELATED DISCOVERY OF SIGNIFICANT PEAKS ............................................. 16
4.2 THE COMPARISON AMONG E-HHTMASS, MASCAP AND HHTMASS2 ........ 18
4.3 CLASSIFICATION BY WEKA ........................................................................ 54
CHAPTER 5 DISCUSSION ............................................................................... 56
REFERENCES ...................................................................................................... 58

參考文獻

1. Egelhofer, V., et al., Protein identification by MALDI-TOF-MS peptide mapping: A new strategy. Analytical Chemistry, 2002. 74(8): p. 1760-1771.
2. Issaq, H.J., et al., The SELDI-TOF MS approach to proteomics: Protein profiling and biomarker identification. Biochemical and Biophysical Research Communications, 2002. 292(3): p. 587-592.
3. De Bock, M., et al., Challenges for biomarker discovery in body fluids using SELDI-TOF-MS. J Biomed Biotechnol, 2010. 2010: p. 906082.
4. Huang, Y.J., et al., SELDI-TOF MS profiling of serum for detection of nasopharyngeal carcinoma. J Exp Clin Cancer Res, 2009. 28: p. 85.
5. Ueda, K., et al., Targeted serum glycoproteomics for the discovery of lung cancer-associated glycosylation disorders using lectin-coupled ProteinChip arrays. Proteomics, 2009. 9(8): p. 2182-92.
6. Shi, L., et al., Discovery and identification of potential biomarkers of pediatric acute lymphoblastic leukemia. Proteome Sci, 2009. 7: p. 7.
7. van Winden, A.W., et al., Validation of previously identified serum biomarkers for breast cancer with SELDI-TOF MS: a case control study. BMC Med Genomics, 2009. 2: p. 4.
8. Ryu, O.H., et al., Identification of parotid salivary biomarkers in Sjogren's syndrome by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry and two-dimensional difference gel electrophoresis. Rheumatology (Oxford), 2006. 45(9): p. 1077-86.
9. Wu, S.P., et al., SELDI-TOF MS profiling of plasma proteins in ovarian cancer. Taiwan J Obstet Gynecol, 2006. 45(1): p. 26-32.
10. Good, D.M., et al., Body fluid proteomics for biomarker discovery: Lessons from the past hold the key to success in the future. Journal of proteome research, 2007. 6(12): p. 4549-4555.
11. Cruz-Marcelo, A., et al., Comparison of algorithms for pre-processing of SELDI-TOF mass spectrometry data. Bioinformatics, 2008. 24(19): p. 2129-36.
12. Mantini, D., et al., A computational platform for MALDI-TOF mass spectrometry data: Application to serum and plasma samples. Journal of Proteomics, 2009. 73(3): p. 562-570.
13. Wu, Z., et al., On the trend, detrending, and variability of nonlinear and nonstationary time series. Proc Natl Acad Sci U S A, 2007. 104(38): p. 14889-94.
14. Wong, J.W.H., G. Cagney, and H.M. Cartwright, SpecAlign - processing and alignment of mass spectra datasets. Bioinformatics, 2005. 21(9): p. 2088-2090.
15. Altamura, S., et al., SELDI-TOF MS detection of urinary hepcidin. Biochimie, 2009. 91(10): p. 1335-1338.
16. Du, P.C., et al., A noise model for mass spectrometry based proteomics. Bioinformatics, 2008. 24(8): p. 1070-1077.
17. Jeffries, N., Algorithms for alignment of mass spectrometry proteomic data. Bioinformatics, 2005. 21(14): p. 3066-3073.
18. Harthoorn, L.F., et al., Salivary biomarkers associated with perceived satiety and body mass in humans. Proteomics Clinical Applications, 2007. 1(12): p. 1637-1650.
19. Jin, G., et al., The knowledge-integrated network biomarkers discovery for major adverse cardiac events. J Proteome Res, 2008. 7(9): p. 4013-21.
20. Guangxu Jin, et al. The network biomarker discovery in prostate cancer from both genomics and proteomics levels. Lecture Notes in Operations Research 9, ed. D.-Z.D.a.X.-S. Zhang. 2008: Lijiang.
21. Tracy, M.B., et al., Precision enhancement of MALDI-TOF MS using high resolution peak detection and label-free alignment. Proteomics, 2008. 8(8): p. 1530-1538.

指導教授

洪炯宗、吳立青
(Jorng-tzong Horng、Li-ching Wu)

審核日期

2010-7-17

推文