自人類基因計畫推行以來，各種大規模基因體定序技術高度發展，已知DNA序列數目大幅增加，這些基因中包含很多重覆出現的序列(Repeat Sequence)。重複序列在醫藥診斷和研究扮演重要的角色。目前已發展一個完整儲存重覆序列的資料庫，將提供國內生物資訊研究者研究及使用，將存放所有生物基因體中有重覆的序列。轉錄因子資料庫存放很多轉錄因子，本文標記轉錄因子於重複序列，應用資料採礦(Data Mining)的關聯性(Association Rule)技術於重複序列中轉錄因子的組合。我們將發現的關聯性規則找出較有意義的，並且去除多餘的規則，並應用關聯性對基因體中的重覆序列進行部份分類(Partial Classification)。我們進行的實驗包含人類第二十二條基因體及其它基因體。在面對生物基因體的研究上，將使我們得到相當有價值的資訊。 Human Genome Project began at 1988 and then lots of genomes will be sequencialized later. Repeat sequences in genome sequences play an important role in medical diagnosis and research. The Transcription factor database TRANSFAC collects many promoter classes. In this thesis, we first mark the transcription factor binding sites in the repeat sequences and then apply data mining techniques to mine the association rules from the combinations of binding sites. We further prune the discovered associations to remove those insignificant associations and find a set of useful rules. Finally, we use the discovered association rules to partially classify the repeat sequences in our repeat database. We also experiment on several genomes including C.Elegans, Human Chromosome 22, and Yeast.
