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    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

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