基因表現的分析對於探索人類疾病的發生與機制一直以來都是一項很有用的工具。許多現有的分析工具針對生物調控網路分析基因表現的資料，但很少有工具探討生物調控網路上面基因組之間的相依關係。我們提出了一個新的方法，可以利用公開的基因表現資料庫所取得的人類疾病資料，來探討生物調控網路上不同交互關係的基因組之間的相依程度。差異表現的基因組可以容易且有效率的在我們的系統上被找到。我們的資料也證實了生物調控網路上基因組之間的相依關係。同時，我們的系統為研究人員提供了一個快速且容易理解的視覺化環境，可以方便的找出生物調控網路與高通量資料之間的關聯性。 Gene expression analysis has been an useful tool for discovering the formation and mechanism of human diseases. Many existing tools analyze gene expression data on biological pathways. But seldom of them discover the correlation of gene pairs on the pathways. We propose a new approach for discovering the correlation between all interactions of gene pairs in a pathway using gene expression profiles acquired from public domain databases related to human diseases. Differentially expressed gene pairs can easily and efficiently found on our system. The results of our data confirm the correlation between gene pairs in the pathways. Our system also provides a prompt visualization and insight into finding the connections between high-throughput data and pathway relations for researchers.
