整合組織基因與蛋白質表現資料以提升化合物疾病關係推論分析

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題名:	整合組織基因與蛋白質表現資料以提升化合物疾病關係推論分析 Integration of tissue-specific gene and protein expression data to improve chemical-disease inference
作者:	王千綸 Wang, Chien-Lun
貢獻者:	智慧數據應用產業碩士專班童俊維謝邦昌
關鍵詞:	基因表現;蛋白質表現;預測毒理學;組織特異性;生物資訊學;資料庫 Gene Expression;Protein Expression;Predictive Toxicology;Tissue Specificity;Bioinformatics;Database
日期:	2021-06-08
上傳時間:	2022-03-03 11:33:00 (UTC+8)
摘要:	世界衛生組織 (World Health Organization) 指出，心血管疾病 (Cardiovascular diseases , CVDs) 是全球最大死因，2019 年造成全球約 1,790 萬人死亡，占死亡總人數的 32%，在 1,700 萬非因傳染性疾病導致的過早死亡案例(七十歲以下)中，有 38% 歸屬心血管疾病。另根據衛生福利部 108 年度死因統計，心臟病是國人第二號殺手，造成 19,859 人死亡，較 107 年度更有 1.1%的增幅趨勢。人類在日常生活中，會接觸到成千上萬可能致病的化合物，而針對日益嚴重的疾病威脅，多數化合物不僅缺乏相關毒性數據也未執行毒物風險評估。然而，大量進行實驗操作驗證，不僅曠日費時，亦需耗費大量資源與成本。故以化合物疾病推論分析系統 (Chemical-disease inference System , ChemDIS) ，以識別化學品相關潛在毒性風險，並產生可作檢驗的假設，以達到加速危害識別過程之效。惟預測毒理學瓶頸之一係為富集化分析後會產生許多可能結果，而過多的預測結果亦使得後續的研究過程更加複雜與困難，此研究的目的是希望利用 KNIME 平台架構 ChemDIS 系統，並以比較毒理基因體學資料庫 CTD (Comparative Toxicogenomics Database) 建立的化合物疾病關係編審資料來驗證在加入基因與蛋白質表現量資料後，能否提升化合物疾病關係推論的預測表現，藉此降低後續分析與驗證的難度。本研究以心血管相關疾病為首要的分析類別來進行概念驗證，未來可拓展至其他疾病做進一步探討。日後可供後續的研究者設計實驗驗證並作為相關毒理資料庫之功能參考。 The World Health Organization (WHO) points out that cardiovascular diseases (CVDs) are the world’s largest cause of death, causing approximately 17.9 million deaths worldwide in 2019, accounting for 32% of the total deaths. Out of the 17 million premature deaths (under the age of 70) due to noncommunicable diseases in 2019, 38% were caused by CVDs. In addition, according to the Ministry of Health and Welfare's statistics on the cause of death in 108 years, heart disease is the second killer of citizens, causing 19,859 deaths, which increases 1.1% compared to the previous year. At the same time, humans are exposed to thousands of chemical substances in everyday life. Facing increasingly serious threats of diseases, most of them lack the toxicological data and have not performed the risk assessment. However, a large number of experimental operations is not only time-consuming but also consumes a lot of resources and costs. Therefore, the Chemical-disease inference System (ChemDIS) is used to identify potential toxicity risks related to chemicals and generate testable hypotheses to accelerate the hazard identification process. Although one of the bottlenecks in predicting toxicology is that enrichment analysis will produce many possible results, too many predictive results also make the subsequent research process more complicated and difficult. This research aims to utilize the KNIME platform to build the ChemDIS system. The chemical-disease relationship compilation dataset established by the CTD (Comparative Toxicogenomics Database) is used to verify whether the addition of gene and protein expression data can improve the predictive performance of chemical-disease inferences, thereby reducing the follow-up difficulties of analysis and verification. Our research uses cardiovascular-related disease data as the primary analysis category for proof of concept, and it can be expanded to other diseases for further discussion in the future. It can also be used by subsequent researchers to design experimental verification and serve as a functional reference for the relevant toxicology database.
描述:	碩士指導教授：童俊維指導教授：謝邦昌委員：王家琪委員：張詠淳委員：許明暉
資料類型:	thesis
顯示於類別:	[大數據科技及管理研究所] 博碩士論文

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