Regularized Estimation of Large-Scale Gene Association Networks using Graphical Gaussian Models

摘要：

We combinine regularized regression methods with the estimation of Graphical Gaussian models. A key issue is the estimation of the matrix of partial correlations for high-dimensional data. Since the (Moore-Penrose) inverse of the sample covariance matrix leads to poor estimates in this scenario, standard methods are inappropriate and adequate regularization techniques are needed. The investigated framework includes various existing regression methods (Lasso, Partial Least Squares) as well as two new approaches based on Ridge Regression and adaptive Lasso, respectively. These methods are extensively compared both qualitatively and quantitatively within a simulation study. In addition, all proposed algorithms are implemented in the R package parcor11, available from the r repository CRAN. This work is based on a joint project with Juliane SchSfer (University of Basel/ETh Zurich) and Anne-Laure Boulesteix (University of Munich). A full paper-including the application to six diverse real data sets-is available (Kramer, Schafer & Boulesteix, 2009).

关键词： graphical models regression sparsity testing gene ezpression data

作者: Nicole Kramer

作者单位: Machine Learning Group, Berlin Institute of Technology

会议类型: 国际会议

会议名称: The 6th International Conference on Partial Least Squares and Related Methods(第六届偏最小二乘及相关方法国际会议)

会议地点: 北京

会议语种:英文

页码: 367-369

在线出版日期: 2009-09-04（万方平台首次上网日期，不代表论文的发表时间）