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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/26130


    Title: 從健保資料庫中疾病之關連發現基因網路、蛋白質之交互作用、疾病網路及藥物目標蛋白網路,-於健保資料庫中利用藥物目標蛋白網路分析國人之共病性
    Authors: 陳玉菁
    Contributors: 資訊學院;生物與醫學資訊學系
    Keywords: 藥物目標蛋白網路;共病性;台灣健康保險資料庫;Drug-target network;Comorbidity;National health insurance database
    Date: 2011
    Issue Date: 2013-07-18 15:33:20 (UTC+8)
    Abstract: 生物體由細胞所組成,而細胞藉由其內的基因、蛋白質、代謝物與離子間藉著生物化學、生理的 交互作用構成一個複雜的網路,使得生物體能執行各式各樣的功能。因此當生物體罹患疾病時,表 示其內的基因、蛋白質或其它分子有缺失或於體內無法達到平衡;已知細胞內網路之交互作用環環 相扣以達成生命的協調,所以當一疾病的發生時會對身體內相關之網絡造成影響,引發其它疾病的 產生;即所謂的共病性(comorbidity)。所以共病性是指兩個不同的疾病發生在同一個體身上的機率 比隨機發生機率要高,且有統計上的意義。因此對於共病性的研究,可幫助我們了解疾病與疾病間 之關聯性;即某疾病發生時極容易引發某些特定疾病產生或與其一同呈現。 先前共病性的研究,因資料取得不易,集中在少數特定疾病間的探討;或是特定疾病與風險因子 或用藥情形等的相互關性;且不論是根據病人本身共病性程度評估或是使用大量族群資料來研究疾 病間之關聯性,都是將共病的事實利用統計的方法呈現出其關聯性,以得到共病的結論;然後並無 法直接解釋共病的原因。 而本研究試圖從藥物目標蛋白網路(Drug-target Network)中找到具有相同結合之目標蛋白質卻 治療不同疾病之藥物;藉此推測並解釋這些不同疾病間的共病關係。此外本研究能使用台灣中央健 康保險局之健康保險資料庫,故進一步將從藥物目標蛋白質網路所推測出共病關係之疾病對應於健 保資料庫中之國際疾病分類號(ICD-9-CM);如此便能從樣本數研究確定共病存在的事實。因此本 研究從藥物目標蛋白網路與台灣健保資料庫的整合來探討疾病間之共病性現象。 此研究的主要目標有: 1. 利用藥物銀行資料庫建立藥物目標蛋白網路。 2. 將台灣健康保險資料庫之藥品代碼對應於藥物銀行資料庫之藥品編號。 3. 利用所建立之藥物目標蛋白網路推測疾病間之共病性現象。 4. 利用台灣健康保險資料庫內容計算國人之共病性關係。 此計畫的目是希望能有系統性的了解對於疾病間之共病性關係,也盼藉由此研究的結果幫助國人 與醫護人員,對疾病有全面性的認識,如疾病之發展史與疾病之演化機制等;並協助醫護人員對特 定疾病可能引發的相關疾病作有效的預防、診斷與治療。相信長遠下來可幫助國人注重個人身體健 康,降低就醫次數,減少整體健保費用之支出;對於國人的健康、醫療品質能有一定的提升與幫助。

    Organism is composed by cells, and the cells can perform various functions by their complex networks of genes, proteins, metabolites and ionic those interact through biochemical and physical interactions. When an organism has a disease, it means its inner genes, proteins and other molecules might be defect or can not be balanced in the networks; therefore, it will trigger the co-emergence of multiple disease in a patient. The comorbidity refers to the statistical association of two distinct diseases in the same individual at a rate higher than expected by chance. Hence, the study of comorbidity can help us more understand associations within diseases. In the previous study of comorbidity, the medical data are not easy to collect; therefore, the study of comorbidity is focused on few specific diseases, association with risk factors or relationship with drug usages. No matter by evaluation degree of patients’ comorbidity or population-based research on comorbidity; they just only reveal the facts of comorbidity by statistics, and can’t explain it directly. This research try to use Drug-target network to infer comorbidity by finding drugs, which cure different diseases but bind with the same target protein, according to these relationships between drugs and same binding protein can imply the comorbidity. Further, list these diseases according to the International Classification of Diseases, Ninth Revision Classification Modification code (ICD-9-CM), because, this project can access the Taiwan national health insurance database. Finally, use the ICD-9-CM code and find the number of patients in Taiwan national health insurance database; then, comorbidity can be calculated by population-based study. Thus, we quantify the comorbidity in mapping population-based study and Drug-target network into each other. The specific aims are: 1. Construct Drug-target network by using DrugBank database. 2. Reflect the codes of drugs in Taiwan national health insurance database to the codes in DrugBank. 3. Infer the comorbidity from Drug-target network. 4. Use Taiwan national health insurance database to calculate the associations of diseases. The purpose of this project is to more understand comorbidity systematically, and helps people and medical persons realize diseases more completely, for example, history of diseases or mechanisms of diseases. Furthermore, it can also help medical persons prevent, diagnose and treat specific disease efficiently and correctly. In a long term, this research can help people more realize self-health, decrease frequency of outpatient, reduce the cost of medical expenses, and finally improve our medical quality.
    Appears in Collections:[生物資訊與醫學工程學系 ] 科技部研究計畫

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