Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/60830
標題: 利用關聯規則與類神經網路研究 神經細胞鈣離子之螢光強度變化
Study on fluorescence intensity change of calcium concentration in neuron cells using association rules and artificial neural network
作者: 丁羿嫣
Ting, I-Yen
關鍵字: association rule
關聯規則
artifical neuron network
calcium concentration
Na+/K+-ATPase
類神經網路
鈣離子濃度
鈉鉀幫浦
出版社: 基因體暨生物資訊學研究所
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摘要: 強心配醣體哇巴因(Ouabain)為治療心臟疾病之用藥,作用機制是抑制鈉鉀幫浦(Na+/K+-ATPase, NKA),進而啟動鈉鈣交換子(Na+/Ca2+ exchanger, NCX),使細胞內鈣離子濃度增加,利用調控細胞內鈣離子濃度,而促進細胞增殖或凋亡雙重效果。丹參中藥材其水萃物酚酸B鎂鹽(Magnesium lithospermate B, MLB)同樣具有抑制鈉鉀幫浦的作用,由於兩者皆是抑制鈉鉀幫浦,卻在濃度高低及時間長短的不同條件作用下,細胞活性有極大差異性,本研究運用關聯式探勘來解釋MLB與Ouabain對神經細胞內鈣離子的濃度作用情形,另一方面,利用類神經網路預測後段時間的螢光強度,可供實驗者考量數據準確性的一個依據。 關聯式探勘方法用來得知某時間點的螢光強度關聯到其他時間的螢光強度,由發現到的隱藏關聯來規劃進行下一步研究方向,為了方便觀察整體時間的螢光強度變化,於是製作鈣離子螢光強度時間圖,將關聯式所提及高頻率項目的時間和濃度,對照其時間點並標記上升或下降符號可方便分析藥物的變化,並得知抑制內質網IP3鈣離子通道的藥劑APB可使丹參萃取物MLB抑制鈣離子濃度上升,及抑制鈉鈣交換子的藥劑KBR對強心配醣體Ouabain使鈣離子濃度上升沒有明顯抑制效果。 使用類神經網路方法預測實驗數據,願景是省下生物實驗研究者大量時間與處理數據的繁鎖步驟。將資料前置處理後得到的兩種模組擁有高準確率,將運用至類神經網路方法來預測後面時間的螢光強度,此結果仍有不錯準確率。本研究因使用的此數據時間間隔不同,故建立模組時需限制在同一時間條件下才可運行預測。
The cardiac glycoside ouabain has been used to treat heart disease. The therapeutic effect of cardiac glycosides lies in their reversible inhibition on the Na+/K+-ATPase of myocardium. An inhibition on Na+/K+-ATPase leads to the elevation of intracellular Na+ concentration, which in turn activates a Na+/Ca2+ exchanger resulting in an increase of intracellular Ca2+ level. Danshen, which is a traditional Chinese herbal medicine, has been widely used in the treatment of cardiac diseases. Magnesium lithospermate B (MLB) is the major water-soluble compound isolated from Danshen. It was assumed to inhibit the activity of Na+/K+-ATPase like the cardiac glycoside ouabain. Using association rules mining to explain intracellular concentration of Ca2+ situation when MLB and Ouabain effect on neuron cell ( SHSY-5Y). Using Artificial Neural Network method to predict fluorescence intensity of behind time, providing experimenter an criteria for data accuracy. Association rule mining is used to know the association between fluorescence intensity of certain time and some time, and the purpose is for further research. Making fluorescence intensity graphs of calcium to see the fluorescence intensity in whole time length conveniently, putting the up and down symbol on the graph from associationd time and intensity. Explain affect of drugs from graph, we discover two things. APB drug of inhibit IP3 recepter is a strong inhibiter to MLB, and KBR drug of inhibit Na+/Ca2+ exchanger doesn't inhibit well to Ouabain. Using Artificial Neural Network method to predict data of experience is the vision for saving time and messy process of analyzing data. We can get two models with high precision from preprocess data, and using those two to predict fluorescence intensity by Artificial Neural Network method. We can get nice precision of predicting fluorescence intensity. But it can only be allow using same time model to process prediction.
URI: http://hdl.handle.net/11455/60830
其他識別: U0005-1908201013564600
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1908201013564600
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