根據質譜儀資料辨識大腸桿菌抗藥性之特徵峰值

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Title:	根據質譜儀資料辨識大腸桿菌抗藥性之特徵峰值
Authors:	姚君翰;Yao, Chun-Han
Contributors:	資訊工程學系
Keywords:	基質輔助雷射脫附電離飛行時間質譜法;大腸桿菌;機器學習;抗生素抗藥性
Date:	2019-07-10
Issue Date:	2019-09-03 15:32:43 (UTC+8)
Publisher:	國立中央大學
Abstract:	大腸桿菌（Escherichia coli）是常見的革蘭氏陰性菌，亦為引起尿路感染之主因，如未能及時施用適當之抗生素治療，可能引發嚴重菌血症進而導致死亡。近年來，基質輔助雷射脫附電離飛行時間質譜法（matrix-assisted laser desorption ionization-time of flight mass spectrometry，MALDI-TOF MS）已被廣泛用於微生物之鑑定，亦有研究用以探究細菌產生抗藥性之原因。然而，目前仍缺乏直接依據質譜資料預測大腸桿菌抗藥性之特徵峰值。本研究藉由長庚醫院多年蒐集之臨床大腸桿菌質譜資料，採用兩階段架構並基於機器學習方法分別建立辨識大腸桿菌對五種抗生素（阿莫西林，頭孢他啶，環丙沙星，頭孢曲松，頭孢呋辛）抗藥性之模型，接著，再進一步分析大腸桿菌對不同抗生素之抗藥性的特徵峰值與其分佈情況。第一階段之隨機森林方法，發現當菌株具有9714或4533之峰值，辨識有無抗藥模型較佳；第二階段針對具有此特徵峰值菌株建立之辨識抗藥模型以極限梯度提升（eXtreme Gradient Boosting）表現為最佳，其中，五種抗生素抗藥之接收者操作特徵曲線下面積（Area Under the Receiver Operating Characteristic Curve）分別為0.62、 0.72、0.87、0.72，以及0.72。此外，除了峰值(單位為m/z) 9714和4533外，6809、7393、7650、8447、10475、10534，以及11783亦為辨識大腸桿菌對抗生素有無抗藥之重要特徵峰值。本研究建立之辨識抗藥模型可及時提供臨床醫師用藥之參考，同時，特徵峰值的分析亦可提供微生物實驗找尋對應之蛋白質片段，以探究產生抗藥之原因。;Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) is a widely adopted method for bacteria species identification and also be reported can determinate the antibiotic resistance and reducing the time antibiotics susceptibility test (AST) cost. However, there has a lack of research for identifying and analyzing antibiotic-resistant Escherichia coli (E. coli), a common and well-known bacterium, by MALDI-TOF MS. Therefore, we construct machine learning models for E. coli antibiotic resistance prediction based on the MS data provided by Chang Gung Memorial Hospital. In our study, we build an alignment algorithm to handle the MS shifting problem and peak detection. We performed the two stage model training: split the dataset and build models for predicting resistance of five antibiotics, amoxicillin, ceftazidime, ciprofloxacin, ceftriaxone and cefuroxime by using logistic regression, support vector machine, random forest and eXtreme Gradient Boosting (XGBoost). XGBoost has the best performance in four machine learning models and achieve 0.62, 0.72, 0.87, 0.72, 0.72 AUC respectively in the subset which presents informative peak m/z 4533 for AMC and m/z 9714 for the rest four antibiotics. Furthermore, we identified the informative peaks for resistance determination of five antibiotics. In short, we build the antibiotic resistance prediction models of E. coli which can support doctor to make medication decision, also the analysis of informative peaks provide a worthy field to research further.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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