臺灣博碩士論文加值系統

蛋白質磷酸化位點周圍序列的分析對於蛋白質與生物學相關研究而言是非常重要的議題，但目前資料探勘技術應用於蛋白質磷酸化位點的研究多以建立分類預測模型的方式進行磷酸化位點預測，因此本研究希望能藉由探索叢集分析技術在蛋白質磷酸化位點序列上的應用，幫助生物醫學研究者篩選、過濾出可能的研究標的，減少研究中所需耗費的時間與人力等資源。
自組織映射網路是目前經常被使用且被證實應用在生物醫學資料上效能良好的叢集分析方法，本研究將蛋白質磷酸化位點序列資料利用傳統的二進位編碼以及胺基酸物理化學特性編碼等兩種方法進行資料編碼，並使用不同拓樸結構的自組織映射網路探索特定蛋白質激酶作用下的蛋白質磷酸化位點周圍序列資料。研究中針對PKA激酶群以及CK2激酶群對應的蛋白質磷酸化位點周圍序列資料進行叢集分析，最後透過五種評估指標以及兩種顏色表示法對兩種資料編碼方式之叢集分析結果進行評估、比較與分析，結果顯示以胺基酸物理化學特性進行資料編碼的叢集分析結果對於有相同特性的胺基酸序列有較好的分群能力。

The analysis of the surrounding sequences of protein phosphorylation sites is a very important topic in the protein and biology related research. However, the current data mining techniques applied on protein phosphorylation sites research mostly focus on building classification models to forecast the phosphorylation sites. This research applies cluster analysis in protein phosphorylation site sequences to assist biomedical researchers to filter through possible targets with reduced time and effort.
Self-organizing map (SOM) is an often used cluster analysis method in biomedical data processing and with proven effectiveness. This thesis used the physical-chemical properties of amino acids and binary coding to encode protein phosphorylation site sequences; different topology of SOM is also used to explore the protein phosphorylation site surrounding sequences data which regulated by specific protein kinase. This research conducts cluster analyses on the PKA group kinase-related and CK2 group kinase-related protein phosphorylation site sequence data. The cluster analysis results of two data coding methods were evaluated, compared, and analyzed by five evaluation indicators and two color representation methods, and the results show that the cluster analysis on data coding using the physical-chemical properties of amino acid can better separate amino acid sequence of similar properties.

目錄
第一章 緒論 - 1 -
1.1 研究背景 - 1 -
1.2 研究動機與目的- 3 -
1.3 研究限制 - 5 -
1.4 論文架構 - 5 -
第二章 文獻探討 - 8 -
2.1 蛋白質與生物相關資訊 - 8 -
2.2 蛋白質磷酸化 - 11 -
2.3 叢集分析方法簡介 - 14 -
2.4 SOM叢集分析方法 - 20 -
第三章 研究方法 - 23 -
3.1 資料分析流程 - 24 -
3.2 研究資料 - 30 -
3.3 資料編碼方式- 32 -
3.4 研究工具 - 36 -
3.5 效能評估方式 - 38 -
第四章 實驗結果 - 46 -
4.1 效能評估結果 - 46 -
4.2 PKA激酶群之叢集分析結果 - 55 -
4.3 CK2激酶群之叢集分析結果 - 60 -
4.4 兩種蛋白質激酶的混合資料之叢集分析結果 - 66 -
第五章 結論與未來展望 - 72 -
5.1 結論 - 72 -
5.2 未來展望 - 73 -
參考文獻 - 74 -

表目錄
表 2-1 二十種胺基酸與其密碼子 - 10 -
表 2-2 三個群集分析方法的差異 - 15 -
表 3-1 蛋白質磷酸化位點序列資料 - 30 -
表 3-2 蛋白質激酶與基質蛋白質對應資料 - 30 -
表 3-3 胺基酸物理化學特性編碼表 - 34 -
表 3-4 二進位編碼表 - 35 -
表 3-5 CINEMA胺基酸顏色表示法 - 39 -
表 3-6 Lesk胺基酸顏色表示法 - 39 -
表 4-1 PKA激酶群序列資料之叢集分析結果－物理化學特性編碼 - 55-
表 4-2 PKA激酶群序列資料之叢集分析結果－二進位編碼 - 58 -
表 4-3 CK2激酶群序列資料之叢集分析結果－胺基酸化學特性編碼 - 61 -
表 4-4 CK2激酶群序列資料之叢集分析結果－二進位編碼 - 64 -
表 4-5 混合激酶序列資料之叢集分析結果－物理化學特性編碼 - 67 -
表 4-6 混合激酶序列資料之叢集分析結果－二進位編碼 - 69 -

圖目錄
圖 1-1 論文架構圖 - 5 -
圖 2-1 蛋白質磷酸化在基因調控機制的示意圖 - 12 -
圖 2-2 SOM概念圖 - 21 -
圖 3-1 研究流程 - 24 -
圖 3-2 資料分析流程圖 - 25 -
圖 3-3 JavaSOM package程式介面 - 28 -
圖 3-4 胺基酸殘基資料格式範例圖 - 31 -
圖 3-5 各胺基酸之物理化學特性- 32 -
圖 3-6 Frequency Logo的範例圖 - 38 -
圖 3-7 準確度測試流程 - 42 -
圖 4-1 兩種編碼方式之Categorical Utility比較 - 48 -
圖 4-2 兩種編碼方式之外部變數的顯著性檢定比較 - 48 -
圖 4-3 兩種編碼方式之準確度比較 - 49 -
圖 4-4 PKA激酶群之群集內亂度結果比較- CINEMA表示法 - 50 -
圖 4-5 PKA激酶群之群集內亂度結果比較- Lesk表示法 - 50 -
圖 4-6 CK2激酶群之群集內亂度結果比較- CINEMA表示法 - 51 -
圖 4-7 CK2激酶群之群集內亂度結果比較- Lesk表示法 - 51 -
圖 4-8 PKA激酶群之群集內相對相似度結果- CINEMA表示法- 52 -
圖 4-9 PKA激酶群之群集內相對相似度結果- Lesk表示法 - 53 -
圖 4-10 CK2激酶群之群集內相對相似度結果- CINEMA表示法- 53 -
圖 4-11 CK2激酶群之群集內相對相似度結果- Lesk表示法 - 54 -

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