Elastic Net Constrained Stereotype Logit Model for Ordered Categorical Data.

ABSTRACT: Gene expression studies are of growing importance in the field of medicine. In fact, sub-types within the same disease have been shown to have differing gene expression profiles. Often, researchers are interested in differentiating a disease by a categorical classification indicative of disease progression. For example, it may be of interest to identify genes that are associated with progression and to accurately predict the state of progression using gene expression data. One challenge when modeling microarray gene expression data is that there are more genes (variables) than there are observations. In addition, the genes usually demonstrate a complex variance-covariance structure. Therefore, modeling a categorical variable reflecting disease progression using gene expression data present

SUBMITTER: Williams AA 

PROVIDER: S-EPMC6063369 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature