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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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/96131

Title:	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
Authors:	楊騏鴻;Yang, Chi-Hung
Contributors:	生醫科學與工程學系
Keywords:	類風濕性關節炎;蛋白質體數據;LC-MS/MS;機器學習;Rheumatoid Arthritis;proteomic data;LC-MS/MS;Machine Learning
Date:	2025-01-02
Issue Date:	2025-04-09 16:12:45 (UTC+8)
Publisher:	國立中央大學
Abstract:	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)。;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).
Appears in Collections:	[Institute of Biomedical Engineering] Electronic Thesis & Dissertation

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