IPF-LASSO: Integrative L₁-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data.

ABSTRACT: As modern biotechnologies advance, it has become increasingly frequent that different modalities of high-dimensional molecular data (termed "omics" data in this paper), such as gene expression, methylation, and copy number, are collected from the same patient cohort to predict the clinical outcome. While prediction based on omics data has been widely studied in the last fifteen years, little has been done in the statistical literature on the integration of multiple omics modalities to select a subset of variables for prediction, which is a critical task in personalized medicine. In this paper, we propose a simple penalized regression method to address this problem by assigning different penalty factors to different data modalities for feature selection and prediction. The penalty factors c

SUBMITTER: Boulesteix AL 

PROVIDER: S-EPMC5435977 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature