首页> 外文会议>Second Critical Assessment of Microarray Data Analysis (CAMDA'01) Oct, 2001 null >MODELING PHARMACOGENOMICS OF THE NCI-60 ANTICANCER DATA SET: UTILIZING KERNEL PLS TO CORRELATE THE MICROARRAY DATA TO THERAPEUTIC RESPONSES
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MODELING PHARMACOGENOMICS OF THE NCI-60 ANTICANCER DATA SET: UTILIZING KERNEL PLS TO CORRELATE THE MICROARRAY DATA TO THERAPEUTIC RESPONSES

机译:NCI-60抗癌药数据集的药物遗传学建模:利用核PLS将微阵列数据与治疗反应相关联

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Modeling the relationship between genomic features and therapeutic response is of central interest in pharmacogenomics [Musumarra et al., 2001]. The NCI-60 cancer data set with both gene expression and drug activity measurements provides an excellent opportunity for this modeling exercise. To correlate the gene expression profile with the drug activity pattern, we utilized a soft modeling technique called Partial Least Squares (PLS) [Tobias, 2000]. Soft modeling requires less stringent assumptions about the data than other modeling techniques [Falk et al., 1992]. A high level of collinearity in multidimensional gene expression profiles motivates us to undertake the PLS approach, which not only trims data redundancy but also exposes the underlying hidden functional units as latent features. It is believed that these functional gene groups play a key role in determining the efficacy of the cancer drugs to different cell lines (types of cancer). We have shown the efficacy of PLS in identifying drug resistant and drug sensitive genes. We have also investigated techniques to exploit the non-linear dependence between individual gene expressions in order to explain variations in the drug activity pattern. This is facilitated by a kernel function that implicitly carries out the regression in a higher-dimensional space where the data is linear [Christiannini et al., 2000]. The kernel-based non-linear approach is shown to be more effective in defining the correlation between the drug response and the gene expressions. The PLS approach, as implemented here, could be used to differentiate cancer cell lines between renal cancer and melanoma, for example, or different drug groups like Alkylating agents and Tubulin-active anti-mitotic agents.
机译:在药物基因组学中,对基因组特征与治疗反应之间的关系进行建模是至关重要的[Musumarra et al。,2001]。 NCI-60癌症数据集(包括基因表达和药物活性测量)为该建模练习提供了极好的机会。为了使基因表达谱与药物活性模式相关,我们利用了一种称为偏最小二乘(PLS)的软建模技术[Tobias,2000]。与其他建模技术相比,软建模不需要那么严格的数据假设[Falk等,1992]。多维基因表达谱中的高共线性促使我们采取PLS方法,该方法不仅可以修剪数据冗余,而且可以将潜在的隐藏功能单元暴露为潜在特征。据信,这些功能基因组在确定抗癌药物对不同细胞系(癌症类型)的功效中起关键作用。我们已经显示了PLS在鉴定耐药性和药物敏感性基因中的功效。我们还研究了利用各个基因表达之间的非线性依赖性的技术,以解释药物活性模式的变化。内核函数有助于在数据为线性的高维空间中隐式执行回归[Christiannini et al。,2000]。基于核的非线性方法显示出在定义药物反应与基因表达之间的相关性方面更有效。如此处实施的PLS方法可用于区分肾癌和黑色素瘤之间的癌细胞系,或者用于区分不同的药物组,例如烷基化剂和微管蛋白活性抗有丝分裂剂。

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