English  |  正體中文  |  简体中文  |  Items with full text/Total items : 90437/105768 (86%)
Visitors : 10936922      Online Users : 561
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

    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/112737

    Title: Incorporating Statistical Test and Machine Intelligence Into Strain Typing of Staphylococcus haemolyticus Based on Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry
    Authors: Chun, Chia-Ru;Chung, Chia-Ru;Wan, Hsin-Yao;Wang, Hsin-Yao;Lien, Frank;Lien, Frank;Tseng, Yi-Ju;Tseng, Yi-Ju;Chun-Hsien, Chun-Hsien C;Chen, Chun-Hsien;Lee, Tzong-Yi;Lee, Tzong-Yi;Li, Tsui-Ping;Liu, Tsui-Ping;洪炯宗;Lu, Jang-Jih;Lu, Jang-Jih
    Contributors: 生物資訊與醫學工程學系
    Keywords: Fisher's exact test;MALDI-TOF MS;Staphylococcus haemolyticus;machine learning;strain typing.
    Date: 2019-09
    Issue Date: 2020-08-27 14:51:02 (UTC+8)
    Publisher: 亞洲大學
    Abstract: Staphylococcus haemolyticus is one of the most significant coagulase-negative staphylococci, and it often causes severe infections. Rapid strain typing of pathogenic S. haemolyticus is indispensable in modern public health infectious disease control, facilitating the identification of the origin of infections to prevent further infectious outbreak. Rapid identification enables the effective control of pathogenic infections, which is tremendously beneficial to critically ill patients. However, the existing strain typing methods, such as multi-locus sequencing, are of relatively high cost and comparatively time-consuming. A practical method for the rapid strain typing of pathogens, suitable for routine use in clinics and hospitals, is still not available. Matrix-assisted laser desorption ionization-time of flight mass spectrometry combined with machine learning approaches is a promising method to carry out rapid strain typing. In this study, we developed a statistical test-based method to determine the reference spectrum when dealing with alignment of mass spectra datasets, and constructed machine learning-based classifiers for categorizing different strains of S. haemolyticus. The area under the receiver operating characteristic curve and accuracy of multi-class predictions were 0.848 and 0.866, respectively. Additionally, we employed a variety of statistical tests and feature-selection strategies to identify the discriminative peaks that can substantially contribute to strain typing. This study not only incorporates statistical test-based methods to manage the alignment of mass spectra datasets but also provides a practical means to accomplish rapid strain typing of S. haemolyticus.
    Relation: Frontiers in Microbiology
    Appears in Collections:[生物資訊與醫學工程學系 ] 期刊論文

    Files in This Item:

    File Description SizeFormat

    All items in ASIAIR are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback