使用融合的SVM对arrayCGH数据进行分类
Classification of arrayCGH data using a fused SVM
摘要：
动机：基于阵列的比较基因组杂交（arrayCGH）最近已成为一种流行的工具，以确定沿基因组的DNA拷贝数的变化。这些图谱开始用作改善癌症预后或诊断的标记物，这意味着需要对arrayCGH数据进行自动监督分类的方法。像基因表达谱一样，arrayCGH谱的特征在于通常在有限数量的样品上测量的大量变量。然而，由于BAC沿基因组的空间组织，arrayCGH谱具有变量之间的相关性的特定结构。这表明，经典的分类方法，往往是基于少量的判别特征的选择，可能不是最准确的方法，可能不会产生容易解释的预测规则。结果：提出了一种新的阵列CGH数据监督分类方法.该方法是支持向量机（SVM）的一种变体，它将基因组中DNA拷贝数变异的生物学特异性作为先验知识。所得到的分类器是一个稀疏的线性分类器的基础上自动选择的染色体上的区域的数量有限，导致易于解释和识别的基因组的歧视性区域。我们测试这种方法对膀胱和葡萄膜癌的三个分类问题，涉及诊断和预后。我们证明，在分类器上引入新的先验知识不仅会导致更准确的预测，而且还可以识别基因组中已知和新的感兴趣区域。可用性：所有数据和算法都是公开可用的。
英文摘要：
Motivation: Array-based comparative genomic hybridization (arrayCGH) has recently become a popular tool to identify DNA copy number variations along the genome. These profiles are starting to be used as markers to improve prognosis or diagnosis of cancer, which implies that methods for automated supervised classification of arrayCGH data are needed. Like gene expression profiles, arrayCGH profiles are characterized by a large number of variables usually measured on a limited number of samples. However, arrayCGH profiles have a particular structure of correlations between variables, due to the spatial organization of BACs along the genome. This suggests that classical classification methods, often based on the selection of a small number of discriminative features, may not be the most accurate methods and may not produce easily interpretable prediction rules. Results: We propose a new method for supervised classification of arrayCGH data. The method is a variant of support vector machine (SVM) that incorporates the biological specificities of DNA copy number variations along the genome as prior knowledge. The resulting classifier is a sparse linear classifier based on a limited number of regions automatically selected on the chromosomes, leading to easy interpretation and identification of discriminative regions of the genome. We test this method on three classification problems for bladder and uveal cancer, involving both diagnosis and prognosis. We demonstrate that the introduction of the new prior on the classifier leads not only to more accurate predictions, but also to the identification of known and new regions of interest in the genome. Availability: All data and algorithms are publicly available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
