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Related Experiment Video

Updated: May 30, 2026

Using a Classroom-Based Deese Roediger McDermott Paradigm to Assess the Effects of Imagery on False Memories

Using a Classroom-Based Deese Roediger McDermott Paradigm to Assess the Effects of Imagery on False Memories

Published on: November 14, 2018

Can the false-discovery rate be misleading?

Rodrigo Barboza¹, Daniel Cociorva, Tao Xu

¹Systems Engineering and Computer Science Program, COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.

|August 12, 2011

Summary

The standard decoy-database approach in shotgun proteomics may overestimate confidence due to overfitting. A new semi-labeled decoy method statistically identifies and corrects for overfitted results, ensuring reliable protein identification.

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Using a Classroom-Based Deese Roediger McDermott Paradigm to Assess the Effects of Imagery on False Memories

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Published on: November 14, 2018

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

Area of Science:

Proteomics
Bioinformatics
Computational Biology

Background:

The decoy-database approach is the standard for evaluating confidence in shotgun proteomic identification.
This method uses decoy (random) sequences to estimate the false-discovery rate (FDR).
Overfitting can occur, leading to inflated identification numbers that do not reflect the true FDR.

Purpose of the Study:

To demonstrate that the decoy-database approach can be susceptible to overfitting.
To introduce a novel method, the semi-labeled decoy approach, to detect and address overfitting.
To improve the reliability of protein identifications in shotgun proteomics.

Main Methods:

Implementation of the standard decoy-database approach in shotgun proteomic data analysis.
Development and application of a modified semi-labeled decoy strategy.
Statistical analysis to determine the presence and extent of overfitting.

Main Results:

The study reveals that apparent good results from the decoy-database approach can be artifacts of overfitting.
Overfitting inflates protein identification numbers, misrepresenting the actual false-discovery rate.
The semi-labeled decoy approach successfully identifies overfitted results.

Conclusions:

The conventional decoy-database approach may yield misleadingly high confidence scores due to overfitting.
The semi-labeled decoy approach provides a statistically sound method to detect and mitigate overfitting.
This advancement enhances the accuracy and reliability of protein identifications in shotgun proteomics.