English  |  正體中文  |  简体中文  |  Post-Print筆數 : 11 |  Items with full text/Total items : 88987/118697 (75%)
Visitors : 23573167      Online Users : 159
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/30902
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/30902


    Title: 使用AUC特徵選取方法在蛋白質質譜儀資料分類之應用
    An AUC criterion for feature selection on classifying proteomic spectra data
    Authors: 葉勝宗
    Contributors: 張源俊
    郭訓志

    葉勝宗
    Keywords: 表面增強雷射脫附遊離/飛行時間質譜
    特徵選取
    分類
    ROC曲線下面積
    支援向量機
    AUC
    feature selection
    classification
    segmentation
    SELDI
    SVM
    Date: 2005
    Issue Date: 2009-09-14
    Abstract: 表面增強雷射脫附遊離/飛行時間質譜(SELDI-TOF-MS)是種屬於高維度的蛋白質質譜儀資料,主要是用來偵測蛋白質分子的表現。由於SELDI技術的限制,導致掃描出來的質譜儀資料往往存在誤差與雜訊,因此在分析前通常會先針對原始資料進行低階的事前處理,步驟包括去除基線、正規化、峰偵測(peak detection)與峰調準(peak alignment)。本文中所探討前列腺癌資料,可分成正常、良性腫瘤、癌症初期與癌症末期四種類別。我們分析及比較兩筆事前處理的蛋白質質譜資料,包括我們自行處理的以及Adam等人所處理的資料。為了解決SELDI在偵測分子質量時常出現的位移誤差以及同位素的問題,我們提出以”質荷比段落”當作新的特徵變數的想法來進行分析。本文利用「ROC曲線下面積」(AUC)當作選取的準則來挑選出重要的質荷比段落,而分類方法則採用支援向量機(SVM)。在四分類的分類結果中,我們自行處理的事前處理資可以得到訓練資料89%及測試資料63 %的正確率。而Adam等人所處理的事前處理資料,則得到訓練資料94%及測試資料86 %的正確率。本研究結果指出不同事前處理的方法對分類結果確實有影響,同時也驗證了利用”特徵變數段落”的方法來進行分析的可行性。
    The surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) is a technique for presenting the expression of molecular masses. It is obvious that every spectrum has a huge dimension of features. In order to analyze these types of spectra samples, preprocessing steps are necessary. The steps of preprocessing include baseline subtraction, normalization, peak detection, and alignment. In our study, we use a prostate cancer data for demonstration. This prostate cancer data can be classified into four categories, namely, healthy men, benign prostate hyperplasia, early stage prostate cancer, and late stage prostate cancer. We analyzed both the preprocessed data processed by ourselves and the preprocessed data done by Adam et al.. In this thesis, we use segmentations of features as “new features” in attempt to solve problems due to location shifts and isotopes. The selection of important segmentations was based on the values of AUC and the SVM was applied for classification. For four-class classification, 94 % and 86 % of accuracy were obtained for training samples and validation samples, respectively, by using Dr. Adam et al.’s preprocessed data, and 89% for training samples, and 63% for validation samples by using our preprocessed data. This study suggested that the preprocessed method does have effect on classification result and a reasonable classification result can be obtained by using segmentations of features.
    Reference: Aloaydin, E.(2004). Introduction To Machine learning. The MIT Press.
    Adam, B.L., Qu, Y., Davis, J.W., Ward, M.D., Clements, M.A., Cazares, L.H., Semmes, O.J., Schellhammer, P.F., Yasui, Y., Feng, Z., Wright, G.L. Jr.(2002). Serum Protein Fingerprinting Coupled with a Pattern-matching Algorithm Distinguishes Prostate Cancer from Benign Prostate Hyperplasia and Healthy Men. CANCER RESEARCH 62(13), 3609-14.
    Baggerly, K. A., Morris, J. S. and Coombes, K.R.(2004). Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments. Bioinformatics 20(5), 777-85.
    Cortes, C. and Mohri, M.(2003). AUC Optimization vs. Error Rate Minimization. Advances in Neural Information Processing System, 15.
    Conrads, T.P., Zhou, M., Petricoin, E.F.,Liotta,L. and Veenstra, T.D.(2003). Cancer diagnosis using proteomic patterns. Expert Rev Mol Diagn 3(4):411-20
    Drucker, H., Christopher, J. C., Burges, Kaufman, L., Smola, A.J., Vapnik, V.(1996). Support Vector Regression Machines. Neural Information Processing Systems 9, 155-161
    Green, D. M. and Swets, J. A. (1966). Signal Detection Theory and Psychophysics. John Wiley & Sons, New York.
    Hanley, J.A. and McNeil, B. J.(1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29-36.
    Hutchens, T., and Yip, T. (1993). New desorption strategies for the mass spectrometric analysis of macromolecules. Rapid Communications in Mass Spectrometry 7, 576-580.
    Kevin R. Coombes, John M. Koomen, Keith A. Baggerly, Jeffrey S. Morris, and Ryuji Kobayashi. Understanding the characteristics of mass spectrometry data through the use of simulation. Cancer Informatics 2005, 1(1) 41-52.
    Li, J., Zhang, Z., Rosenzweig, J., Wang, Y.Y., Chan, D.W.(2002). Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer. Clinical Chemistry 48, 1296-1304.
    Lilien, R.H., Farid, H. and Donald, B.R.(2003). Probabilistic Disease Classification of Expression-Dependent Proteomic Data from Mass Spectrometry of Human Serum. Journal of Computational Biology 10(6), 925-946.
    Lyons-Weiler, J., Pelikan, R., Zeh,H. J. , Whitcomb, D. C., Malehorn,D. E., Bigbee,W.L., and Hauskrecht M.( 2005).Assessing the Statistical Significance of the Achieved Classification Error of Classifiers Constructed using Serum Peptide Profiles, and a Prescription for Random Sampling Repeated Studies for Massive High-Throughput Genomic and Proteomic Studies. Cancer Informatics 1(1), 53-77.
    Pontil, M., Rifkin, R. and Evgeniou, T.(1999). From Regression to Classification in Support Vector Machines. European Symposium on Artificial Neural Networks.
    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., Liotta, L.A.(2002). Use of proteomic patterns in serum to identify ovarian cancer. Lancet 359, 572-577.
    Qu, Y., Adam, B.L., Thornquist, M., Potter, J.D., Thompson, M.L., Yasui, Y., Davis, J., Schellhammer,P. F., Cazares,L., Clements,M.A., Wright, Jr.G.L., and Feng, Z.(2003).Data Reduction Using a Discrete Wavelet Transform in Discriminant Analysis of Very High Dimensionality Data. Biometrics 59, 143–151.
    Reddy, G. and Dalmasso E. A. (2003). SELDI ProteinChip Array Technology: Protein-Based Predictive Medicine and Drug Discovery Applications. Journal of Biomedicine and Biotechnology 4, 237-241
    Tang, N., Tornatore, P. & Weinberger, S.R. (2004). Current developments in SELDI affinity technology. Mass Spec. Rev. 23, 34−44.
    Vapnik, V. (1995) .The Nature of Statistical Learning Theory. Springer Verlag,.
    Description: 碩士
    國立政治大學
    統計研究所
    93354016
    94
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0093354016
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

    Files in This Item:

    File SizeFormat
    index.html0KbHTML61View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback