Enhancing the Lasso Approach for Developing a Survival Prediction Model Based on Gene Expression Data.

ABSTRACT: In the past decade, researchers in oncology have sought to develop survival prediction models using gene expression data. The least absolute shrinkage and selection operator (lasso) has been widely used to select genes that truly correlated with a patient's survival. The lasso selects genes for prediction by shrinking a large number of coefficients of the candidate genes towards zero based on a tuning parameter that is often determined by a cross-validation (CV). However, this method can pass over (or fail to identify) true positive genes (i.e., it identifies false negatives) in certain instances, because the lasso tends to favor the development of a simple prediction model. Here, we attempt to monitor the identification of false negatives by developing a method for estimating the number o

SUBMITTER: Kaneko S 

PROVIDER: S-EPMC4469838 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature