生物反應路徑為目前相當重要的研究，而在生物反應路徑中，信號傳導扮演著極重要的角色，磷酸化反應為主要的信傳導方式，因此探討磷酸化反應直接或間接對其他蛋白質的影響，可對生物反應路徑有更進一步的瞭解。然而，現今天研究中，尚未提供一個工具建立與疾病相關之信號傳導蛋白質交互作用網路，因此在本論文中，我們提出以疾病相關之蛋白激酶為核心化簡蛋白質交互作用網路之工具。本論文先建立與疾病相關聯之蛋白激酶交互作用網路，並將此交互作用網路擴張以得到蛋白激酶直接或間接對其他蛋白質的影響之資訊，接著我們採用「六度分離」理論化簡此網路中的雜訊，並且整合疾病之微陣列實驗資料，利用視覺化呈現給使用者。
本論文提供查詢以疾病相關之蛋白激酶為核心化簡蛋白質交互作用網路之工具，利用此工具，生醫研究者可經由視覺化呈現之蛋白質交互作用網路中，得到可能之調控路徑，並根據分析過之微陣列實驗的結果表現值，得到可能與此疾病相關之異常表現基因。

Studies have shown that signal transduction pathways play key roles in most biological process pathway. Phosphorylation is the critical way of signal transduction. Study the regulation of protein kinase on other proteins can help us to realize biological process pathway further. Besides, we can get more information of disease by combining the protein kinase-related network and disease-related gene expression data. However, the general PPI networks are too complex to see the information in it. In this study, we use statistical method to find disease-related protein kinase and construct protein kinase-related network. In order to decrease the network complexity, we apply Six Degrees of Separation theory to refine the network. Finally, we combined this network with microarray data of the disease.
We present a tool to visualize a disease associated kinase PPI network which is called DKNY. Users can see the possibly regulated path with analyzed express data by using DKNY.

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