GUEST: an R package for handling estimation of graphical structure and multiclassification for error-prone gene expression data.

ABSTRACT:

Summary

In bioinformatics studies, understanding the network structure of gene expression variables is one of the main interests. In the framework of data science, graphical models have been widely used to characterize the dependence structure among multivariate random variables. However, the gene expression data possibly suffer from ultrahigh-dimensionality and measurement error, which make the detection of network structure challenging and difficult. The other important application of gene expression variables is to provide information to classify subjects into various tumors or diseases. In supervised learning, while linear discriminant analysis is a commonly used approach, the conventional implementation is limited in precisely measured variables and computation of their invers

SUBMITTER: Chen LP 

PROVIDER: S-EPMC11655624 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature