利用機器學習分析類風濕性關節炎相關蛋白質資料以探討疾病生物標記

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姓名

楊騏鴻(Chi-Hung Yang) 查詢紙本館藏

畢業系所

生醫科學與工程學系

論文名稱

利用機器學習分析類風濕性關節炎相關蛋白質資料以探討疾病生物標記
(Using machine learning to analyze Rheumatoid Arthritis-related proteomics data for exploring disease biomarkers)

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至系統瀏覽論文 (2029-8-1以後開放)

摘要(中)

本研究旨在透過分析不同國家及不同物種的類風濕性關節炎相關蛋白質資料，並深入探討其致病機制及潛在生物標記。我們首先從ProteomeXchange網站中蒐集到了五筆公開的蛋白質體資料，包括四筆來自中國北京、中國廣州、荷蘭和巴基斯坦的人類資料，以及一筆來自小鼠的資料。這些資料都是液相層析-串聯質譜儀(LC-MS/MS)所生成的。接著，我們將這些資料丟入FragPipe進行定量分析，再根據分析出的數據進一步利用傳統生物資訊方法與機器學習尋找可能的致病蛋白，最終篩選出三個潛在生物標記，包含：血清澱粉樣蛋白A1 (SAA1)、觸珠蛋白(HPT)及纖維蛋白原α鏈(FIBA)。

摘要(英)

This study aims to explore the pathogenesis of Rheumatoid Arthritis (RA) and identify potential biomarkers associated with RA by analyzing proteomics data from different countries and species. We collected five public proteomics datasets from ProteomeXchange, including four datasets from human in Beijing, Guangzhou, the Netherlands, and Pakistani, as well as one dataset from mice. These datasets were generated using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Next, we processed the data using FragPipe for quantitative analysis, followed by traditional bioinformatics methods and machine learning to identify potential disease-related proteins. These methods led to the identification of three potential biomarkers: serum amyloid A1 (SAA1), haptoglobin (HPT), and fibrinogen alpha chain (FIBA).

關鍵字(中)

★ 類風濕性關節炎
★ 蛋白質體數據
★ LC-MS/MS
★ 機器學習

關鍵字(英)

★ Rheumatoid Arthritis
★ proteomic data
★ LC-MS/MS
★ Machine Learning

論文目次

目錄
中文摘要 i
Abstract ii
致謝 iii
目錄 v
圖目錄 vii
表目錄 viii
一、緒論 1
1-1 類風濕性關節炎 1
1-2 機器學習 3
1-3 差異表達分析 5
1-4 研究動機 7
二、材料方法 9
2-1 類風濕性關節炎蛋白質資料來源 9
2-2 資料預處理與參數設定 12
2-3 差異表達蛋白鑑定 14
2-4 特徵重要性分析與外部驗證 16
三、分析流程 20
四、實驗結果 22
4-1 差異表達分析 22
4-2 富集分析 28
4-3 蛋白質交互作用網絡分析 32
4-4 機器學習相關分析 34
4-4-1 模型評估 34
4-4-2 特徵重要性分析 42
4-4-3 外部驗證 48
4-5 分析總結 54
五、結論 56
六、研究限制 58
七、未來展望 59
參考文獻 60
附錄 64
縮寫表 69

參考文獻

參考文獻
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指導教授

張彙音(Hui-Yin Chang)

審核日期

2025-1-2

推文