人類基因體序列草稿的完成，使得科學家可以利用電腦計算的方法來預測及分析人們所關注的基因。因而現今在研究基因對影響人類各式各樣的疾病和癌症中，生物資訊法已是其中一項不可缺的工具。利用已知的癌症相關基因之物理特性及功能性區域，可以被用來研究這些基因在癌化過成中所扮演的角色。這個研究的目的是要利用電腦計算搜尋的策略，來辨認新穎的癌症相關基因。在第一階段的研究中，利用PubMed資料庫透過資料探戡的方式來尋找及辨識已知的致癌及抑癌基因。然後再利用一個半自動的資訊擷取系統，到幾個特定資料庫收集目標基因的特定資訊，並儲存到為本次研究所建立的癌症相關基因(TAG)資料庫中。在第二階段的研究中，我們為TAG資料庫設計便利的網頁介面，提供方便的功能幫助使用者搜尋TAG資料庫裏的資訊及分析蛋白質可能與癌化相關的資訊。在第三階段的研究中，我們利用所收集到的癌症相關蛋白質之功能性區域資訊，建立一個加權值量表。最後藉由搜尋比對在人類資料庫中完整的cDNA序列，配合已建立的功能性區域之加權值量表，來辨認出新穎的癌症相關基因。目前在TAG資料庫中總共收集到476個癌症相關基因，並且已經提供了完善的網頁界面。在辨認新穎的癌症相關基因研究中，在比對分析完整的cDNA序列後，有18個侯選基因的加權值大於已建立的致癌基因訓練樣本之最大加權值(19.45)。其中6個是已收集到的致癌基因；1個是已知的致癌基因，但未收錄到TAG資料庫中。其餘11個中，有5個基因是屬於非受體蛋白酪氨酸激酶家族成員。利用這個方法我們可以找出更多跟癌症有相關的基因。

The completion of human genome sequences is applied to predict and analyze genes of interest through using computational approach. It is now well recognized that bioinformatics is one of the essential tool in studying genes involved in various human diseases including cancer. The available annotations including physical characterization and functional domains of known tumor-related genes thus can be used to study the role of genes involved in carcinogenesis. This study aims to identify novel tumor-associated genes (TAGs) using a computational searching strategy. In the first phase of this study, target genes were identified though text-mining approach in the PubMed database. A semi-automatic information retrieving engine was designed to collect specific information of the target genes from various web resources and store in the TAG database. In the second phase of this study, a user-friendly web interface was designed to provide functions in searching and analysis of TAG data. In the third phase of this study, the TAG information is analyzed by building up domain weighting profiles that can specify the feature of particular type of oncoproteins. Finally, the profile can be used to identify novel TAGs by searching against currently available cDNA sequences in the human genome database. We have collected 476 TAGs in the database and the web interface that provides user-friendly environment for searching information in the TAG database has been built. In finding novel gene of this study, there are 18 candidate genes domain weight profile score higher than maximum score of TAG training set of oncogene (19.45) after analysis cDNA sequence. That including 6 known oncogenes within TAG database, 1 gene is oncogene without TAG database, and 11 known genes without TAG database that including 5 genes belong to Tec family kinase protein. Using this method can help us to finding more tumor-related genes.

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