利用微陣列擾動數據建置之MAPK Signal Transduction Pathway生物路徑預測系統

MAPK Signal Transduction Pathway Biological Pathway Prediction System Based on Microarry Perturbation Data

陳怡仲(Yi-Chung Chen) ； 林秉賢(Pin-Hsien Lin) ； 吳家樂(Ka-Lok Ng)

《2008資通技術管理與應用會議》 2008 (2008/12) Pp. 86-92

摘要

MAPK訊號傳遞路徑的機制廣佈於真核生物細胞中，且受外在環境變化而調控其轉錄作用。本研究是利用酵母菌基因之微陣列晶片實驗資料，來預測MAPK Signaling Transduction路徑組成順序。我們利用了兩組實驗數據，一組包含了與訊號傳遞路徑有關的56種實驗，另一組則包含了300種不同突變與化學處理。計算方法是計算預測的路徑中基因(mRNA)與鄰近的基因(mRNA)表達量的皮爾森相關係數絕對值總和分數，然後將分數做排序後，最高分的路徑為預測之MAPK Signal Transduction路徑。然後，我們再加入Protein Protein Interaction (PPI)資料，來提昇路徑預測的排名。由預測結果得知，在加入PPI資料後，真實MAPK路徑會落在所有可能路徑前15%中，為相當好的預測。最後我們將此方法應用在蛋白質複合體的系統，預測組成蛋白質複合體的各個子單元順序。
我們將目前所作的資料建置成網頁，且提供下列功能:(i)預測最有可能的MAPK Signal Transduction路徑，按皮爾森相關係數絕對值總和後的高低排名，(ii)基因與基因間兩兩之Pearson相關的值。

關鍵字

蛋白質交互作用 ； 微陣列晶片數據 ； 皮爾森Pearson相關係數 ； 訊號傳遞路徑

並列摘要

MAPK Signal Transduction Pathway mechanism widespread in eukaryotic cells, and changes in the external environment and to control its registered role. This study is the use of yeast gene microarray chip experimental data to predict the path of MAPK Signal Transduction order. We used two sets of experimental data, a group containing the signal transduction pathway and the 56 kinds of experiments, the other group includes 300 different mutations and chemical treatment. Is calculated in terms of the forecast path (mRNA) and the nearby Gene (mRNA) expression of the Pearson correlation coefficient of absolute sum of scores, then scores do sort, the maximum points to predict the path of MAPK Signal Transduction Pathway. Then, we will join the Protein Protein Interaction (PPI) data, to upgrade path forecast rankings. The results from that, after joining the PPI data, real MAPK path will fall on all possible paths before the 15 percent, to very good forecast. Finally, we use this method in the protein complex systems, forecast a protein complex composed of various sub-unit order.
We will now built into the data page, and offers the following features: (i) forecast the most likely path MAPK Signal Transduction, according to Pearson correlation coefficient sum of the absolute level position, (ii) between genes and gene February 2 of the Pearson correlation value.

並列關鍵字

protein interactions ； microarray chip data ； Pearson correlation coefficient ； MAPK Signal Transduction Pathway