基於質譜儀資料利用人工智慧方法辨識革蘭氏陰性菌對環丙沙星抗藥性之特徵峰值

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：12

、訪客IP：3.129.70.254

姓名

張文睿(Wen-Rui Zhang) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

基於質譜儀資料利用人工智慧方法辨識革蘭氏陰性菌對環丙沙星抗藥性之特徵峰值
(Identification of Informative Peaks for Ciprofloxacin-resistant Gram-negative Bacteria Based on MALDI-TOF MS Using Artificial Intelligence Approaches)

相關論文

★ 基於質譜儀資料使用機器學習辨識克雷伯氏肺炎桿菌之多重抗藥性	★ 結合多種訊號預處理方法於質譜儀資料以辨識細菌對抗生素之抗藥性
★ 利用機器學習預測濁水溪沖積扇區域之地下水位	★ 使用表徵學習和機器學習方法於晶圓線切割機台之異常偵測
★ 應用數位分身於馬達軸承之異常偵測	★ 基於光誘導介電泳影像處理檢測流體抗藥性
★ 利用機器學習方法基於多類型地層監測資料預測濁水溪沖積扇地區之地層下陷	★ 基於人工智慧模型預測抗菌肽的最小抑菌濃度於特定菌株上
★ 使用語言模型嵌入和不平衡調整之深度學習方法識別多功能抗菌肽	★ 使用權重組合模型預測雲林縣地層下陷
★ 基於深度學習從核醣核酸定序表達譜推斷外周血單核細胞之細胞組成

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

抗生素耐藥性已成為全球重要且日益嚴重的威脅，耐藥病原體感染會造成大量發病率和死亡率對患者構成威脅。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）為一種新興的微生物分析技術，被廣泛應用於微生物菌種之鑑定，近年來亦有許多研究用以辨識細菌之抗藥性。目前文獻上的研究侷限於使用質譜資料來辨識某一革蘭氏陰性菌或革蘭氏陽性菌對環丙沙星(Ciprofloxacin)之抗藥性，仍少有使用質譜來辨識多種革蘭氏陰性菌對環丙沙星抗藥性特徵峰值之研究。本研究藉由長庚醫院蒐集的大量質譜資料，並針對四種革蘭氏陰性菌: Escherichia coli、 Klebsiella pneumoniae、 Acinetobacter baumannii 、與Acinetobacter nosocomialis，採用兩種特徵擷取方式並搭配隨機森林，卷積神經網路建構抗藥性之模型，接著再進一步分析不同特徵選取方式對於四種革蘭氏陰性菌對環丙沙星之抗藥性特徵峰值與其分布差異。在各個細菌中，皆以卷積神經網路建構之模型有較
高之準確率，其在獨立測試的準確率分別為76.58%，75.49%，85.78%，88.52%。此外，發現峰值2918、4244、7928、8553 m/z重複出現在A. baumannii 和A. nosocomialis中，峰值3580、3926、6327 m/z 重複出現在A. baumannii 和E.coli中，峰值8351 m/z 重複出現在A. nosocomialis 和E.coli中，以及在K. pneumoniae中的峰值4570、5410、6153、7705 m/z出現在其他細菌上。對於A. baumannii有最多8個峰值在其他革蘭氏陰性菌上出現;但沒有存在三種或所有革蘭氏陰性菌皆出現之特徵峰值，這表示了在環丙沙星作用機制下，四種革蘭氏陰性菌仍保留其獨特峰值。本研究四種革蘭氏陰性菌特徵峰值分析可找尋對應蛋白質片段，以探究抗藥之原因。

摘要(英)

MALDI-TOF MS is an emerging microbial analysis technology, which is widely used in the identification of bacterial species. However, there is lacking research to identify and analyze the informative peak of ciprofloxacin resistance Gram-negative bacteria. In this study, we col-lected a lot of mass spectrometry data and considered four Gram-negative bacteria (Esche-richia coli, Klebsiella pneumoniae, Acinetobacter baumannii, Acinetobacter nosocomialis), using two feature extraction methods, binning method, and kernel density estimation to process the MS data. After feature extraction, we constructed random forest and convolutional neural network model to predict antibiotic resistance, and then further analyze the informative peaks and distribution. In independent test, the binning method and convolutional neural network model has the highest accuracy 76.58%, 75.49%, 85.78%, and 88.52% for E. coli, K. pneu-moniae, A. baumannii and A. nosocomialis respectively. After feature selection, it was found that the peaks 2918, 4244, 7928, and 8553 m/z repeatedly appeared in A. baumannii and A. nosocomialis, the peaks 3580, 3926, and 6327 m/z repeatedly appeared in A. baumannii and E. coli, the peak 8351 m/z repeatedly appeared in A. nosocomialis and E. coli. And the peaks of 4570, 5410, 6153, and 7705 m/z of K. pneumoniae appeared on other bacteria. For A. baumannii, there are most 8 informative peaks appearing on the other three Gram-negative bacteria. However, there are no exist informative peaks that appear in three or all Gram-negative bacteria, which means that under the mechanism of action on ciprofloxacin, the four Gram-negative bacteria retain their unique peaks. In short, the analysis of informative peaks provide a worthy field to research further.

關鍵字(中)

★ 基質輔助雷射脫附電離飛行時間質譜法
★ 人工智慧
★ 抗生素抗藥性

關鍵字(英)

★ MALDI-TOF MS
★ artificial intelligence approach
★ antibiotic resistance

論文目次

中文摘要 i
Abstract ii
致謝 iii
Table of Contents iv
List of Figures vi
List of Tables viii
Chapter 1 Introduction 1
1.1 Background 1
1.2 Related Works 3
1.3 Motivation and Goal 6
Chapter 2 Materials and Methods 7
2.1 Data Collection and Preprocessing 8
2.2 Feature Extraction 9
2.2.1 Kernel Density Estimation (KDE) 9
2.2.2 Binning Method 10
2.3 Model Building 11
2.3.1 Random Forest (RF) 11
2.3.2 Convolutional Neural Network (CNN) 12
2.4 Feature Selection 13
2.4.1 Grad-weighted Class Activation Mapping (Grad-CAM) 13
2.4.2 Ensemble-based Method 15
2.5 Evaluation Metrics 16
Chapter 3 Results 18
3.1 Experimental Result Presentation 18
3.1.1 Peak Counts 18
3.1.2 Peaks Distribution 18
3.2 Performances of Models 23
3.3 Analysis of Informative Peaks 24
3.3.1 Peak Distribution of Grad-CAM 24
3.3.2 Peak Distribution of Ensemble-based Method 28
3.3.3 Two Feature Selection Methods on Random Forest 29
Chapter 4 Discussions and Conclusions 37
4.1 Discussions 37
4.2 Conclusions 38
References 39

參考文獻

[1] Kluytmans, J., Van Belkum, A., and Verbrugh, H., "Nasal carriage of Staphylococcus aureus: epidemiology, underlying mechanisms, and associated risks," Clinical microbiology reviews, vol. 10, no. 3, pp. 505-520, 1997.
[2] Laxminarayan, R. et al., "Antibiotic resistance—the need for global solutions," The Lancet infectious diseases, vol. 13, no. 12, pp. 1057-1098, 2013.
[3] Bizzini, A., Durussel, C., Bille, J., Greub, G., and Prod′Hom, G., "Performance of matrix-assisted laser desorption ionization-time of flight mass spectrometry for identification of bacterial strains routinely isolated in a clinical microbiology laboratory," Journal of clinical microbiology, vol. 48, no. 5, p. 1549, 2010.
[4] Stepien-Pysniak, D., Hauschild, T., Rozanski, P., and Marek, A., "MALDI-TOF mass spectrometry as a useful tool for identification of Enterococcus spp. from wild birds and differentiation of closely related species," Journal of microbiology and biotechnology, vol. 27, no. 6, pp. 1128-1137, 2017.
[5] Li, M. et al., "Rapid antimicrobial susceptibility testing by matrix-assisted laser desorption ionization–time of flight mass spectrometry using a qualitative method in Acinetobacter baumannii complex," Journal of microbiological methods, vol. 153, pp. 60-65, 2018.
[6] Weis, C. et al., "Direct Antimicrobial Resistance Prediction from MALDI-TOF mass spectra profile in clinical isolates through Machine Learning," bioRxiv, 2020.
[7] Peleg, A. Y. and Hooper, D. C., "Hospital-acquired infections due to gram-negative bacteria," New England Journal of Medicine, vol. 362, no. 19, pp. 1804-1813, 2010.
[8] Campoli-Richards, D. M., Monk, J. P., Price, A., Benfield, P., Todd, P. A., and Ward, A., "Ciprofloxacin," Drugs, vol. 35, no. 4, pp. 373-447, 1988.
[9] West, H. J., "Novel precision medicine trial designs: umbrellas and baskets," JAMA oncology, vol. 3, no. 3, pp. 423-423, 2017.
[10] Angeletti, S. et al., "MALDI-TOF mass spectrometry and blakpc gene phylogenetic analysis of an outbreak of carbapenem-resistant K. pneumoniae strains," New Microbiol, vol. 38, no. 4, pp. 541-550, 2015.
[11] Chang, K.-C. et al., "Direct detection of carbapenemase-associated proteins of Acinetobacter baumannii using nanodiamonds coupled with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry," Journal of microbiological methods, vol. 147, pp. 36-42, 2018.
[12] van Oosten, L. N. and Klein, C. D., "Machine learning in mass spectrometry: a MALDI-TOF MS approach to phenotypic antibacterial screening," Journal of medicinal chemistry, vol. 63, no. 16, pp. 8849-8856, 2020.
[13] Sousa, T. d. et al., "Putative protein biomarkers of Escherichia coli antibiotic multiresistance identified by MALDI mass spectrometry," Biology, vol. 9, no. 3, p. 56, 2020.
[14] Huang, T.-S., Lee, S. S.-J., Lee, C.-C., and Chang, F.-C., "Detection of carbapenem-resistant Klebsiella pneumoniae on the basis of matrix-assisted laser desorption ionization time-of-flight mass spectrometry by using supervised machine learning approach," PloS one, vol. 15, no. 2, p. e0228459, 2020.
[15] 姚君翰, "根據質譜儀資料辨識大腸桿菌抗藥性之特徵峰值," 碩士, 資訊工程學系, 國立中央大學, 桃園縣, 2019.
[16] Wang, H.-Y. et al., "A large-scale investigation and identification of methicillin-resistant Staphylococcus aureus based on peaks binning of matrix-assisted laser desorption ionization-time of flight MS spectra," Briefings in bioinformatics, vol. 22, no. 3, p. bbaa138, 2021.
[17] Pedregosa, F. et al., "Scikit-learn: Machine learning in Python," the Journal of machine Learning research, vol. 12, pp. 2825-2830, 2011.
[18] O′Shea, K. and Nash, R., "An introduction to convolutional neural networks," arXiv preprint arXiv:1511.08458, 2015.
[19] Albawi, S., Mohammed, T. A., and Al-Zawi, S., "Understanding of a convolutional neural network," in 2017 International Conference on Engineering and Technology (ICET), 2017, pp. 1-6: Ieee.
[20] Dash, M. and Liu, H., "Feature selection for classification," Intelligent data analysis, vol. 1, no. 1-4, pp. 131-156, 1997.
[21] Selvaraju, R. R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., and Batra, D., "Grad-cam: Why did you say that?," arXiv preprint arXiv:1611.07450, 2016.
[22] Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D., "Grad-cam: Visual explanations from deep networks via gradient-based localization," in Proceedings of the IEEE international conference on computer vision, 2017, pp. 618-626.
[23] Tuv, E., Borisov, A., and Torkkola, K., "Feature selection using ensemble based ranking against artificial contrasts," in The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006, pp. 2181-2186: IEEE.
[24] Fluss, R., Faraggi, D., and Reiser, B., "Estimation of the Youden Index and its associated cutoff point," Biometrical Journal: Journal of Mathematical Methods in Biosciences, vol. 47, no. 4, pp. 458-472, 2005.

指導教授

洪炯宗吳立青(Jorng-Tzong Horng Li-Ching Wu)

審核日期

2021-7-23

推文