未來研究方向

在本研究所提之方法中，每一個步驟都具有調整的彈性。3.4 節中我們將基因表現資料 分成 4 種不同的表現程度，但是這並不一定是個準則。分階選擇的不同會影響實驗的 精確度，因此，將來可以利用其他的方式(如 SOM)選用不同的分階，討論不同的分階 對於實驗所帶來的影響；3.5 節中，我們使用全局排比(global alignment)將轉換後之基 因資料作排比。在未來的研究中，可以選用局部排比(local alignment)或者半全局排比 (semi-global alignment)來擷取部份的基因表現，讓基因間的關係更為彈性；評分矩陣的 選用，往往是依據經驗法則，如 3.5 節中，我們的評分矩陣亦是如此。但是隨著已知調 控模組的增加，我們可以使用其他的學習方法(如基因規劃法，Genetic Programming)，

修正評分矩陣，如此必定可以大幅度改進實驗預測之準確性。

如 2.3 節所述，生物上的許多問題，往往無法單獨倚靠計算機或統計等方法解決，需要 加入生物上的資訊才能獲得更好的結果。如 TNP 利用 SCPD 中整理之調控因子結合區 序列，掃描基因之上游區，作為實驗方法得前處理，如此大幅度降低其 false positive，

進而提升預測的準確程度。因此，加入適當之生物資訊，亦可是將來繼續努力方向之 一。

結合多種實驗(或方法)重建調控網路，一直被許多研究人員所採用的方式。Hsu 將 TNP 與 PROSPECT(Fujibuchi et al. 2001)的結合、本研究與 TNP 之結合。將適當的實驗方法 結合，亦可改善實驗的結果。因此，如何將前人研究之優點，與本研究結合，亦是未 來可行的努力目標。

附錄一：26 個轉錄調控模組，其字串排比之得分分布。

GCN4

MATalpha2

PUT3

Repressor of CAR1

0

SWI5

0 500 1000 1500

-50 -40 -30 -20 -10 0 10 20 30 40 50 得分

個數

TBP

0 500 1000 1500 2000

-50 -40 -30 -20 -10 0 10 20 30 40 50 得分

個數

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