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Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...

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Related Experiment Video

Updated: May 28, 2026

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

Stability-based biomarker selection.

Ron Wehrens¹, Pietro Franceschi, Urska Vrhovsek

¹Edmund Mach Foundation, Research and Innovation Center, Via Edmund Mach 1, 38010 San Michele all'Adige (TN), Italy. ron.wehrens@iasma.it

Analytica Chimica Acta

|October 4, 2011

Summary

This summary is machine-generated.

This study introduces a novel strategy for biomarker identification in omics sciences by assessing biomarker stability under data perturbations. This approach enhances the reliability of identifying key variables that distinguish populations, even with limited samples.

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Area of Science:

Omics sciences
Biostatistics
Bioinformatics

Background:

Biomarker identification is crucial for distinguishing between biological populations.
The high-dimensional nature of omics data (more variables than samples) presents significant challenges.
Existing methods often struggle with the variability of error estimates from cross-validation.

Purpose of the Study:

To present a generalizable strategy for robust biomarker identification.
To improve the accuracy and reliability of biomarker discovery in omics studies.
To overcome limitations associated with traditional cross-validation error estimates.

Main Methods:

A novel strategy based on the stability of potential biomarkers under data perturbation.
The proposed method is applicable across various common biomarker identification techniques.
It avoids reliance on potentially unstable error estimates derived from cross-validation.

Main Results:

Demonstrated significant improvements in biomarker identification accuracy.
The stability-based approach enhances the ability to detect true population differences.
The method proves effective even in challenging high-dimensional datasets.

Conclusions:

The proposed data perturbation strategy offers a robust and generalizable method for biomarker discovery.
This approach enhances the reliability of omics data analysis.
It provides a valuable alternative to methods heavily dependent on cross-validation.