Supplement to : Predicting phenotypes from microarrays using amplified , initially marginal , eigenvector regression
Supplement to : Predicting phenotypes from microarrays using amplified , initially marginal , eigenvector regression
复制标题
补充：使用放大的、初始边缘的、特征向量回归从微阵列预测表型
DOI：
--
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
L. Ding
中科院分区：
文献类型：
--
作者：
L. Ding
对于DLBCL数据，我们不仅列出了选定的基因，而且还试图在现有文献中找到有关这些基因的任何讨论。我们最终估计的模型使用了49个基因特征，对应26个基因。为了检查每个选定基因与DLBCL的相关性，我们采用了两种方法。首先努力寻找文献检查的生物学联系的基因鉴定任何类型的淋巴瘤。第二部分列出了统计学、计算机科学和生物信息学（相当长的）方法学文献中使用统计或机器学习方法检查DLBCL数据集的任何参考文献。表1显示了所有26个基因的结果。总而言之，26个基因中有16个已在生物学文献中与淋巴瘤相关，其中19个已通过为DLBCL数据集开发的统计技术确定。虽然这26个基因中的许多先前与淋巴瘤，特别是DLBCL有关，但AIMER确实鉴定了4个基因，其符号为ALDH2， CELF2， COL16A1和DHRS9，这些基因先前未在生物学或方法学文献中被鉴定。我们注意到，虽然我们已经尽了一切努力来定位每个基因，但考虑到关于这个主题的大量和不断发展的文献，那些我们无法定位的基因可能之前已经被研究过了。
For the DLBCL data, we not only list the selected genes, but also attempt to find any discussion of those genes in existing literature. Our final estimated model uses 49 gene features, which correspond to 26 genes. To examine the relevance of each selected gene for DLBCL, we adopt two approaches. The first endeavors to find literature examining the biological connection of the identified gene to any type of lymphoma. The second lists any reference in the (rather lengthy) methodological literature in statistics, computer science, and bioinformatics that uses statistical or machine learning methods to examine the DLBCL dataset. We display our findings for all 26 genes in Table 1. To summarize, 16 out of the 26 genes have been related to lymphoma in the biological literature, and 19 of them have already been identified via statistical techniques developed for the DLBCL dataset. While many of the 26 genes have been previously connected to lymphoma in general and DLBCL in particular, AIMER does identify 4 genes with symbols ALDH2, CELF2, COL16A1, and DHRS9 that have not been previously identified in the biological or methodological literature. We note that, while we have made every effort to locate each gene, given the large and evolving literature on this topic, those we have been unable to locate may have none-the-less been previously studied.
影响因子：
158.5
作者：
Lossos, IS;Czerwinski, DK;Levy, R
通讯作者：
Levy, R
影响因子：
158.5
作者：
Rosenwald, A;Wright, G;Staudt, LM
通讯作者：
Staudt, LM
影响因子：
20.3
作者：
Natkunam, Yasodha;Zhao, Shuchun;Levy, Ronald
通讯作者：
Levy, Ronald