結合生物微晶片實驗與電腦計算分析是目前研究癌症的一項新興科技，藉由上萬個基因表現來預測癌症的各項顯示特徵是否出現，甚至找出規則以了解癌症的成因，影響的方式，並且發展藥物療程來抑制癌症。不只癌症，任何未知的疾病都適用此方法。而利用基因表現的差異來判斷疾病的特徵或成因是目前大部分研究的議題，此議題中單就基因表現量不同所選出來的基因也許數量上非常多，並且無法直接觀察這些基因之間的調控關係，導致生物意義上的顯示不足。
於是我們設計一個方法，先觀察在生物調控網路上，有調控關係的基因，再探討這些基因間，基因表現量的關係分別在乳癌復發與不復發之間的差異性，如此提高資料可信度，並可以觀察出傳統基因表現差異選取無法看出的生物意義。
我們的方法可以在結合不同的實驗資料的情況下，保持極高的穩定度，並達成我們的目的，得到真正有鑑別力的基因與其調控關係，大大降低生物技術實驗的成本。

The method of different expressed genes for microarray gene expression data can help find the factor which affect diseases. But differentially expressed genes are selected may be too many and we can’t get their correlation each other. Therefore, we design a method to attempt to avoid the problem and discover more biological meanings than traditional method. We select a gene pair set of interacting genes in biological pathway and discuss correlations of expressions of gene pairs in different condition (relapse and non-relapse in breast cancer) by microarray gene expression data. Furthermore, we test on breast cancer relapse and non-relapse datasets to demonstrate that our method is useful and reliable according to the good result. And we get very stable result in in the same platform.

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