基於質譜儀資料使用深度學習方法預測不同地區之耐甲氧西林金黃色葡萄球菌之抗藥性

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高士唐(Shi-Tang Kao) 查詢紙本館藏

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論文名稱

基於質譜儀資料使用深度學習方法預測不同地區之耐甲氧西林金黃色葡萄球菌之抗藥性

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摘要(中)

耐甲氧西林金黃色葡萄球菌（Methicillin-Resistant Staphylococcus aureus，MRSA）於1961年在英國首次被識別，是一種對多種抗生素具有抗藥性的細菌，並且有著極高的死亡率和發病率，因此也被稱為“超級細菌”。基質輔助雷射脫附電離飛行時間質譜法（MALDI-TOF MS）提供了一種更快速的微生物鑑定替代方法，當與機器學習結合使用時，能顯著提高微生物鑑定的準確性和效率。不同地理區域的MRSA菌株展示出特定的亞型分布，先前研究多集中於單一地區的MRSA抗藥性預測，而忽略了地理差異對於抗藥性模式的影響，導致模型性能會因為MRSA資料集來源的差異性而有所下降。為了解決這一問題，在本研究中我們使用超過25,000個來自台灣和瑞士的數據樣本，採用深度學習方法並結合聯邦式學習和遷移式學習，對模型進行了跨地區訓練以提升模型的適應性和準確性。結果顯示在獨立測試集的AUROC和AUPRC分別達到了0.82以及0.7以上。我們還通過shapley值和差異表達分析鑑定了幾個與MRSA特有峰值相關的關鍵特徵，比如2410至2419範圍內的2415點代表PSM-mec、6590至6599範圍內的6593點代表SA1452蛋白並進行了台灣和瑞士抗藥性與敏感性樣本之間的比較。最後以宏觀的角度探討了全球MRSA亞型的分布和盛行率，發現與我們的結果相關聯，如台灣的ST59-V和ST239-III，以及瑞士或歐洲的ST22-IV。這些成果不僅豐富了我們對MRSA全球分布的了解，也強調了利用地區化數據提升抗藥性預測準確性的重要性。

摘要(英)

Methicillin-Resistant Staphylococcus aureus (MRSA) was first identified in the UK in 1961. It is a type of bacteria resistant to multiple antibiotics and has a very high mortality and morbidity rate, earning it the label of a “superbug.” Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) provides a faster alternative for microbial identification. When combined with machine learning, it significantly enhances the accuracy and efficiency of microbial identification. MRSA strains from different geographic regions exhibit specific sequence types distribution. Previous studies have primarily focused on predicting MRSA resistance within a single region, neglecting the impact of geographic differences on resistance patterns, leading to decreased model performance due to differences in the sources of MRSA datasets. To address this issue, our study utilized over 25,000 data samples from Taiwan and Switzerland. We employed deep learning methods combined with federated learning and transfer learning to train the model across regions, improving its adaptability and accuracy. The results showed that the AUROC and AUPRC in the independent test sets reached over 0.82 and 0.7, respectively. Through shapley value and differential expression analysis, we identified several key features associated with MRSA specific peaks, such as the 2415 peak in the 2410-2419 range representing PSM-mec and the 6593 peak in the 6590-6599 range representing the SA1452 protein, and conducted a comparative analysis of resistance and susceptible samples from Taiwan and Switzerland. Finally, from a macroscopic perspective, we explored the global distribution and prevalence of MRSA sequence types, finding correlations with our results, such as ST59-V and ST239-III in Taiwan and ST22-IV in Switzerland or Europe. These findings not only enrich our understanding of the global distribution of MRSA but also emphasize the importance of using localized data to improve the accuracy of resistance predictions.

關鍵字(中)

★ 金黃色葡萄球菌
★ 基質輔助雷射脫附電離飛行時間質譜
★ 深度學習
★ 抗生素抗藥性

關鍵字(英)

★ Staphylococcus aureus
★ MALDI-TOF
★ Deep Learning
★ 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 7
1.4 Goal 7
Chapter 2 Materials and Methods 8
2.1 Data Collection and Preprocessing 10
2.1.1 Data Sources 10
2.1.2 MALDI-TOF MS Data and Peak Detection 10
2.1.3 Data Cleansing 11
2.2 Feature Extraction 13
2.2.1 Peak Alignment 13
2.2.2 Binning Method 13
2.2.3 Filtering Features 14
2.2.4 Scaling and Normalizing 14
2.2.5 Data Splitting 15
2.3 Model Building 16
2.3.1 Deep Neural Network (DNN) 16
2.3.2 Federated Learning 17
2.3.3 Transfer Learning 17
2.4 Shapley Value 18
2.5 Differential Expression Analysis 18
2.6 Weighted Gene Co-expression Network Analysis (WGCNA) 18
2.7 Evaluation Metrics 19
Chapter 3 Results 20
3.1 Performances of Models 20
3.1.1 Performances of Training Individually 20
3.1.2 In Comparison with Works in The Literature 21
3.1.3 Performance of Federated Learning and Transfer Learning 22
3.2 Important Features in Different Datasets 23
3.3 Comparative Analysis of Resistant and Susceptible Samples 24
3.3.1 Peaks Between Resistant and Susceptible Samples 24
3.3.2 Observing Resistance and Susceptible Samples Through Dimension Reduction 26
3.4 Analysis of Peak Intensity 26
3.4.1 Analysis of Intensity Differences in Dataset 26
3.4.2 Clustering Peaks with Intensity Using WGCNA 28
3.5 Global MRSA Analysis 33
3.5.1 MRSA Sequence Types Distribution 33
3.5.2 MRSA World’s Trend 36
3.6 Impact of Sample Collection Time on Model Performance 38
Chapter 4 Discussions and Conclusions 40
4.1 Discussions 40
4.2 Conclusions 41
References 42

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指導教授

洪炯宗吳立青(Jorng-Tzong Horng Li-Ching Wu)

審核日期

2024-7-29

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