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    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/61604


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    題名: Inferring Genetic Interactions via A Data-driven Second Order Model
    作者: 洪英超
    Jiang,Ci-Ren;Hung,Ying-Chao;Chen, Chung-Ming;Shieh, Grace S.
    貢獻者: 統計系
    關鍵詞: gene expression;genetic interaction;microarray data;pathway;regression;transcriptional regulatory interaction
    日期: 2012.05
    上傳時間: 2013-11-11 17:45:26 (UTC+8)
    摘要: Genetic/transcriptional regulatory interactions are shown to predict partial components of signaling pathways, which have been recognized as vital to complex human diseases. Both activator (A) and repressor (R) are known to coregulate their common target gene (T). Xu et al. (2002) proposed to model this coregulation by a fixed second order response surface (called the RS algorithm), in which T is a function of A, R, and AR. Unfortunately, the RS algorithm did not result in a sufficient number of genetic interactions (GIs) when it was applied to a group of 51 yeast genes in a pilot study. Thus, we propose a data-driven second order model (DDSOM), an approximation to the non-linear transcriptional interactions, to infer genetic and transcriptional regulatory interactions. For each triplet of genes of interest (A, R, and T), we regress the expression of T at time t + 1 on the expression of A, R, and AR at time t. Next, these well-fitted regression models (viewed as points in R(3)) are collected, and the center of these points is used to identify triples of genes having the A-R-T relationship or GIs. The DDSOM and RS algorithms are first compared on inferring transcriptional compensation interactions of a group of yeast genes in DNA synthesis and DNA repair using microarray gene expression data; the DDSOM algorithm results in higher modified true positive rate (about 75%) than that of the RS algorithm, checked against quantitative RT-polymerase chain reaction results. These validated GIs are reported, among which some coincide with certain interactions in DNA repair and genome instability pathways in yeast. This suggests that the DDSOM algorithm has potential to predict pathway components. Further, both algorithms are applied to predict transcriptional regulatory interactions of 63 yeast genes. Checked against the known transcriptional regulatory interactions queried from TRANSFAC, the proposed also performs better than the RS algorithm.
    關聯: Frontiers in Statistical Genetics and Methodology, 3, Article 71
    資料類型: article
    顯示於類別:[統計學系] 期刊論文

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