| 研究生: |
謝秉恆 Ping-heng Hsieh |
|---|---|
| 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 |
| 指導教授: |
洪炯宗
Jorng-tzong Horng 吳立青 Li-ching Wu |
| 口試委員: | |
| 學位類別: |
碩士 Master |
| 系所名稱: |
資訊電機學院 - 資訊工程學系 Department of Computer Science & Information Engineering |
| 畢業學年度: | 98 |
| 語文別: | 英文 |
| 論文頁數: | 68 |
| 中文關鍵詞: | 生物標記 、質譜圖分析 |
| 外文關鍵詞: | SELDI-TOF MS, MALDI-TOF MS, biomarker |
| 相關次數: | 點閱:9 下載:0 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
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。
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.
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.