Insulin resistance: regression and clustering.
Insulin resistance: regression and clustering.
复制标题
DOI：
10.1371/journal.pone.0094129
复制
发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Olshen RA
中科院分区：
文献类型：
--
作者：
Yoon S;Assimes TL;Quertermous T;Hsiao CF;Chuang LM;Hwu CM;Rajaratnam B;Olshen RA
本文试图对一组中国妇女的胰岛素抵抗（IR）进行精确的定义。我们的定义故意不依赖于身体质量指数（BMI）或年龄，虽然在其他研究中，与特定的随机效应模型很大程度上不同于这里使用的模型，BMI占很大一部分的IR的变化。我们通过应用高斯混合矢量量化（GMVQ），一种聚类技术，开发应用程序的有损数据压缩实现我们的目标。定义数据来自在医疗实践中发挥重要作用的测量。第1节对数据进行了精确说明。详细介绍了他们的家庭结构。它们涉及脂质水平和口服葡萄糖耐量试验（OGTT）的结果。我们将GMVQ应用于从OGTT结果和血脂对年龄和BMI功能的回归中获得的残差，这些功能是从数据中推断的。一个引导程序开发我们的家庭数据的补充，从其他方法的见解使我们相信，两个集群是适当的定义IR精确。一组由IR女性组成，另一组似乎不是。基因和其他特征用于预测聚类成员。我们认为，预测与“主效应”是不令人满意的，但预测，包括相互作用可能。
In this paper we try to define insulin resistance (IR) precisely for a group of Chinese women. Our definition deliberately does not depend upon body mass index (BMI) or age, although in other studies, with particular random effects models quite different from models used here, BMI accounts for a large part of the variability in IR. We accomplish our goal through application of Gauss mixture vector quantization (GMVQ), a technique for clustering that was developed for application to lossy data compression. Defining data come from measurements that play major roles in medical practice. A precise statement of what the data are is in Section 1. Their family structures are described in detail. They concern levels of lipids and the results of an oral glucose tolerance test (OGTT). We apply GMVQ to residuals obtained from regressions of outcomes of an OGTT and lipids on functions of age and BMI that are inferred from the data. A bootstrap procedure developed for our family data supplemented by insights from other approaches leads us to believe that two clusters are appropriate for defining IR precisely. One cluster consists of women who are IR, and the other of women who seem not to be. Genes and other features are used to predict cluster membership. We argue that prediction with “main effects” is not satisfactory, but prediction that includes interactions may be.
登录
查看更多内容
影响因子：
7.7
作者：
GREENFIELD, MS;DOBERNE, L;REAVEN, G
通讯作者：
REAVEN, G
影响因子：
56.9
作者：
Frayling, Timothy M.;Timpson, Nicholas J.;McCarthy, Mark I.
通讯作者：
McCarthy, Mark I.
影响因子：
64.8
作者：
Gibbs, RA;Belmont, JW;Tanaka, T
通讯作者：
Tanaka, T
影响因子：
5.8
作者：
George, Lindsey;Bacha, Fida;Arslanian, Silva
通讯作者：
Arslanian, Silva
DOI：
10.1111/j.2517-6161.1977.tb01600.x
发表时间：
1977-01-01
期刊：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL
影响因子：
--
作者：
DEMPSTER, AP;LAIRD, NM;RUBIN, DB
通讯作者：
RUBIN, DB