Does combining numerous data types in multi-omics data improve or hinder performance in survival prediction? Insights from a large-scale benchmark study.

ABSTRACT:

Background

Predictive modeling based on multi-omics data, which incorporates several types of omics data for the same patients, has shown potential to outperform single-omics predictive modeling. Most research in this domain focuses on incorporating numerous data types, despite the complexity and cost of acquiring them. The prevailing assumption is that increasing the number of data types necessarily improves predictive performance. However, the integration of less informative or redundant data types could potentially hinder this performance. Therefore, identifying the most effective combinations of omics data types that enhance predictive performance is critical for cost-effective and accurate predictions.

Methods

In this study, we systematically evaluated the predictive pe

SUBMITTER: Li Y 

PROVIDER: S-EPMC11370316 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature