Prediction-Oriented Marker Selection (PROMISE): With Application to High-Dimensional Regression.

ABSTRACT: In personalized medicine, biomarkers are used to select therapies with the highest likelihood of success based on an individual patient's biomarker/genomic profile. Two goals are to choose important biomarkers that accurately predict treatment outcomes and to cull unimportant biomarkers to reduce the cost of biological and clinical verifications. These goals are challenging due to the high dimensionality of genomic data. Variable selection methods based on penalized regression (e.g., the lasso and elastic net) have yielded promising results. However, selecting the right amount of penalization is critical to simultaneously achieving these two goals. Standard approaches based on cross-validation (CV) typically provide high prediction accuracy with high true positive rates but at the cost of

SUBMITTER: Kim S 

PROVIDER: S-EPMC5543994 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature