藉由L1000™表達圖譜數據來解釋中醫分類方法中的屬性和歸經;The Attribution and Meridian of Traditional Chinese Medicine (TCM) Classification Method Explained by Analyzing L1000™ Expression Profiling Data

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题名:	藉由L1000™表達圖譜數據來解釋中醫分類方法中的屬性和歸經;The Attribution and Meridian of Traditional Chinese Medicine (TCM) Classification Method Explained by Analyzing L1000™ Expression Profiling Data
作者:	呂天蒙;Lyu, Tian-Meng
贡献者:	系統生物與生物資訊研究所
关键词:	中藥的性味歸經;藥物高通量篩選;生物資訊;抗癌藥物;TCM classification method;High-throughput screening of drugs;Bioinformatics;Anticancer drugs
日期:	2018-08-17
上传时间:	2018-08-31 13:12:52 (UTC+8)
出版者:	國立中央大學
摘要:	中國傳統中醫中藥一直被西方現代科學低估，但隨着從各種中藥中提取出的不同化合物用於治療疾病有着不錯的功效，所以近年來中醫藥引起了醫學領域越來越多的關注。根據中國古代的醫學文獻，中醫藥被歸為三種歸屬：寒，溫和熱藥物;中醫藥同時也被歸經分類，歸經意味著該藥的歸屬於它所影響的經脈。在本研究中，我們試圖通過分析基於中醫藥治療的癌細胞的L1000表達譜數據來解釋中醫藥的藥性與經絡之間的關係。在分析這些數據後，我們發現在各個屬性，味道和歸經的藥物處理後的細胞表現量數據差異的功能性分析來看，其不同群組之間有着不盡相同的功能。此外我們了解到，在一些特定屬性藥物的導致細胞的作用途徑可能影響癌症的發展，根據這個結果，我們選擇其中四種中藥符合我們感興趣的屬性和經絡並且通過細胞生物學實驗證明了其中三種藥物可以抑制乳癌細胞MCF7生長。最後，我們認為我們對L1000概況數據解釋的中醫藥歸因研究結果可能對中醫藥的發展有用。;Chinese traditional Chinese medicine has been underestimated by Western modern science, but with the different compounds extracted from various Chinese medicines used to treat diseases which have good effects, therefore, in recent years, Chinese medicine has attracted more and more attention in the medical field. According to ancient Chinese medical literature, Chinese drug are classified into three attribution: cold, medium and heat medicine; Chinese medicine is also classified as Meridian which means that the drug belongs to the meridians it affects. In this study, we attempted to explain the differences between the attribution and meridian of traditional Chinese medicine by analyzing the L1000 expression profile data of cancer cells treated with Chinese drug. After data analysis, we found that functional analysis of differences between various attributes, tastes, and meridian showed different functions between different groups. In addition, we have learned that the pathways leading to the action of cells in some specific properties may affect the development of cancer. Based on this result, we selected four TCMs which mix the attributes and meridians of our interest and we proved three of these drug’s ability of inhibiting MCF7 breast cancer cells through cell biology experiments. Finally, we believe that the results of Chinese medicine classification method studies that explain by the L1000 profile data may be useful for the development of Chinese medicine.
显示于类别:	[系統生物與生物資訊研究所] 博碩士論文

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