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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Testing the additional predictive value of high-dimensional molecular data.

Anne-Laure Boulesteix¹, Torsten Hothorn

¹Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr 15, D-81377 Munich, Germany. boulesteix@ibe.med.uni-muenchen.de

BMC Bioinformatics

|February 11, 2010

Summary

This summary is machine-generated.

This study introduces a new method to assess the added predictive value of high-dimensional molecular data in disease prediction. The approach effectively determines if complex biological data improves upon existing clinical predictors.

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Area of Science:

Bioinformatics
Biomedical Research
Statistical Modeling

Background:

High-dimensional molecular data, like gene expression, has been used for disease prediction.
The additional predictive value of this data alongside classical predictors is often under-examined.

Purpose of the Study:

To develop and validate a method for assessing the additional predictive value of high-dimensional molecular data.
To provide guidance on parameter selection for the proposed method.

Main Methods:

A permutation-based testing procedure combining logistic regression and boosting regression.
Simulation studies to evaluate the method's power in various scenarios.
Application to publicly available cancer datasets.

Main Results:

The novel approach demonstrates strong statistical power in simulations.
Effective in identifying predictive value from both strong and weak molecular predictors.
Successfully applied to real-world cancer data.

Conclusions:

The developed method offers a computationally efficient way to assess the global predictive power of numerous molecular predictors.
It is valuable when clinical covariates or prognostic indices are already available.
The method is available as the R package "globalboosttest".