Data integration by multi-tuning parameter elastic net regression.

ABSTRACT:

Background

To integrate molecular features from multiple high-throughput platforms in prediction, a regression model that penalizes features from all platforms equally is commonly used. However, data from different platforms are likely to differ in effect sizes, the proportion of predictive features, and correlations structures. Subtle but important features may be missed by shrinking all features equally.

Results

We propose an Elastic net (EN) model with separate tuning parameter penalties for each platform that is fit using standard software. In a comprehensive simulation study, we evaluated the performance of EN logistic regression with multiple tuning penalties. We found that when the number of informative features differs among the platforms, and when there is no notabl

SUBMITTER: Liu J 

PROVIDER: S-EPMC6180486 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature