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    政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 >  Item 140.119/150165
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/150165


    Title: 糖質體學資料探勘與自動化分析
    Data Mining and Automated Analysis in Glycomics
    Authors: 曾偉綱
    Kang, Tseng Wei
    Contributors: 張家銘
    chang, jia-ming
    曾偉綱
    Tseng Wei Kang
    Keywords: 人工智慧
    資料探勘
    質譜儀
    機器學習
    分子結構預測
    Artificial Intelligence
    Data Mining
    Mass Spectrometry
    Machine Learning
    Molecular Structure Prediction
    Date: 2024
    Issue Date: 2024-03-01 13:41:06 (UTC+8)
    Abstract: 本研究致力於透過液相層析串聯質譜(LC-MS/MS)自動識別與分析醣鏈結構。應用資料探勘和機器學習技術,我們分析了斑馬魚腦與卵巢組織的數據集。相較於人工分析,我們的方法顯著提高了從龐大數據中定位樣本的效率,證明了其可行性。特別是,我們的方法大幅縮短了生物學家進行分析的時間,將原本需要30小時的任務縮減至幾秒內完成。這一成就強調了我們方法提升效率的潛力。最終,我們驗證了隨機森林模型在此問題上為各類別提供了最合適的模型,並具有跨組織樣本識別能力。它能有效識別訓練數據中未出現的組織中的醣鏈,證明了這一工具的實用應用性。總之,這項研究為深入理解和分析質譜儀產生的醣數據提供了一種快速且實用的工具,為未來研究的應用和方法的改進開辟了新的途徑。
    This study focused on automatically identifying and analyzing glycan structures based on liquid chromatography/tandem mass spectrometry (LC-MS/MS). By applying data mining and machine learning techniques, we analyzed the zebrafish Brain and Ovary tissue datasets. Our methods enhanced the efficiency of targeting samples from vast data compared to human efforts, thus demonstrating their viability.

    Specifically, our approach has significantly expedited the time-consuming analysis process for a biologist, reducing tasks that traditionally took 30 hours to mere seconds. This achievement underscores the efficiency-enhancing potential of our method.
    Ultimately, we have validated that the Random Forest model in this problem offers a generally most suitable model for various categories and possesses cross-tissue sample identification capabilities. It can effectively recognize glycans in tissues not present in the training data, proving the practical applicability of this tool.
    In summary, the research provides a rapid and pragmatic tool for a deeper understanding and analysis of glycan data produced by mass spectrometers, promising new avenues for future research application and refinement of this method.
    Reference: 1. Interpreting Mass Spectra Retrieved January 2, 2024, from
    Spectrahttps://chem.libretexts.org/Courses/Athabasca_University/Chemistry_350
    %3A_Organic_Chemistry_I/12%3A_Structure_Determination-
    _Mass_Spectrometry_and_Infrared_Spectroscopy/12.02%3A_Interpreting_Mass_Spectra
    2. Urban, J., Jin, C., Thomsson, K. A., Karlsson, N. G., Ives, C. M., Fadda, E., & Bojar, D. (2023). Predicting glycan structure from tandem mass spectrometry via deep learning. bioRxiv. https://doi.org/10.1101/2023.06.13.544793
    3. Burkholz, R., Quackenbush, J., & Bojar, D. (2021). Using graph convolutional neural networks to learn a representation for glycans. Elsevier, Volume 35, Issue
    11, 15 June 2021, 109251.
    4. weka.classifiers.rules OneR Retrieved July 26, 2023, from
    https://weka.sourceforge.io/doc.dev/weka/classifiers/rules/OneR.html
    5. Glycoworkbench. (n.d.). Retrieved July 26, 2023, from
    https://code.google.com/archive/p/glycoworkbench/
    6. Ceroni, A., Maass, K., Geyer, H., Geyer, R., Dell, A., Haslam, SM. GlycoWorkbench: a tool for the computer-assisted annotation of mass spectra of glycans. Journal of Proteome Research. 2008 Apr;7(4):1650-9. doi:
    10.1021/pr7008252. Epub 2008 Mar 1. PMID: 18311910.
    https://pubmed.ncbi.nlm.nih.gov/18311910/
    7. Varki, A., Cummings, R. D., Esko, J. D., Stanley, P., Hart, G. W., Aebi, M., Darvill, A. G., Kinoshita, T., Packer, N. H., Prestegard, J. H., Schnaar, R. L., &
    Seeberger, P. H. (2015). Essentials of Glycobiology. Cold Spring Harbor Laboratory Press. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4104780/
    Description: 碩士
    國立政治大學
    資訊科學系
    108753122
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0108753122
    Data Type: thesis
    Appears in Collections:[資訊科學系] 學位論文

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