闡明基因調控網絡的模組化，是了解複雜生物系統的主要方式。本研究提出一個新的多階式群聚法架構，可以萃取出由同一群調控子所調控的基因，此群基因稱為基因調控模組。本研究所使用的方法，首先從多個帶有時序資訊的基因微陣列資料中取得顯著的共表現證據，接著利用新提出的密集子圖尋找演算法在此共表現網絡上找出密集子圖，同一子圖內的成員即構成一個模組。我們將此方法應用到模擬的資料與真實的酵母菌基因微陣列資料，結果顯示以此方法所得到的模組，可以有效的還原調控網絡的拓樸結構，並且擁有高度一致性的基因功能註解；此外，在同一模組中，藉由基因的啟動序列分析，我們也可以發現顯著的結合樣式，這些結合樣式在文獻中所報導的主要功能，也與模組的主要基因註解一致，證明了本方法所得到的模組，是具有顯著生物意義的。

Deciphering the modularity of transcriptional networks is a principle approach to understand this complex biological system. We purpose a multi-step clustering scheme to extract sets of genes regulated by the same set of transcription factors. These sets of genes are defined as transcriptional modules. In our approach, we first obtain significant evidences of co-expression from multiple microarray time profiles, and then extract sets of genes which forms dense sub-graph on the co-expression networks by a novel method called Clique-based Dense Sub-graph Finding Algorithm. We apply this scheme to artificial data and real microarray datasets of Saccharomyces cerevisiae. The resulted modules can potently imply the topological structure of transcriptional networks, and also have significant annotated function, component, or process. In the promoter sequence analysis, we can also find significant binding motifs, and have agreement on function between resulted modules and known motifs.

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