本研究的目的可分成三個部份做討論：首先，鑒於現今對使用者操作上不夠方便和系統仍無法與其他資料庫結合的缺點，我們在此提出一個能讓使用者查詢所需資料比現在設計更簡易的搜尋機制。基於建置於Gene Navigator (GenNav)資料庫的豐富資訊，我們建構一個比現今查詢機制更友善的介面。有鑒於目前整合各式各樣資料庫的趨勢，該查詢機制對研究人員而言，擁有高度延展性。其次，疾病信號傳導路徑的快速搜尋機制是基於Kyoto Encyclopedia of Genes and Genomes (KEGG) 資料庫。為了改善KEGG太過繁複的弊病，吾人致力於將該資料庫區分成三類：疾病類型、成長因數及信號傳導路徑。研究人員能藉由該機制找出不同疾病之間的關聯性。根據意外的發現，他們可以設計新的實驗來驗證查詢到的結果，以便擴展完整的代謝途徑。為了要讓查詢的程式自動執行，我們透過ASP.NET設計查詢流程並應用座標概念來達成系統設計。再者，透過SQL Server Enterprise Manager建構出查詢資料。接下來多工搜尋引擎是透由C#.NET所實作出來。為了改善來自不同搜尋引擎所造成重複資料的缺點，我們特設計一套涵蓋Google, Vivisimo, Baidu, Yahoo及GAIS網站的查詢系統。該平臺的初始設定包括：搜尋引擎選擇、依序排序的方法、逾時的重複嘗試次數、多執行緒的數目以及單次搜尋頁數。如果該系統可以結合前兩個機制，那系統化的結構同時可以提供結構性的資料庫及非結構性的網路資源。另外，最常被使用到的分子生物資料庫詳列於附錄，可以提供新進人員詳細的資訊。資訊數位化及網際網路上搜尋資料庫的發展迅速，相關生物、藥物、醫學研究方面的資訊及分析工具已經累積相當豐富。最後，我們衷心企盼在未來該整合性查詢機制能被廣泛應用在醫學界、製藥及先進的科技上。

The purpose of this study can divide three parts to discuss. First, based on the abundant information established in Gene Navigator (GenNav) database, we construct one more friendly interface than the current query mechanism. Whereas the trend of integrating various databases currently, this query mechanism has highly extension for researchers. Second, quick query mechanism of disease signaling pathway is based on Kyoto Encyclopedia of Genes and Genomes (KEGG) database. For improving the complicated drawback of KEGG, we devote on dividing the database into three categories: disease category, growth factor, and signaling pathway. Researchers can
find out the connection of different diseases through query mechanism. According to the unexpected discovery, they can design new experiments to verify the query results, so as to stretch the current metabolism pathway completely. We design the query process and manipulate coordinate concepts via ASP.NET in order to execute the query procedure automatically. Furthermore, the query information is constructed via SQL Server Enterprise Manager. Third, Meta Search Engine is implemented by C#.NET. In order to improve the disadvantage of repeated materials from different search engines, we design one query system that combines Google, Vivisimo, Baidu, Yahoo, and GAIS websites. The initial settlement of platform includes: selective search engine, sort methods in an order, repeatedly trying times in exceeding time, numbers of multiple thread, and search webpage in single time. If this system can combine with the first two mechanisms, the systematical structure may offer researchers structural databases and non-structural network resources simultaneously. Moreover, the most common used Molecular Biology Database Collection is listed in appendix of this study that can provide newcomers the detailed information. Finally, we expect genuinely this integrated query mechanism can be used to medical science, pharmacy, and the advanced technology widely in the future.

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