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Updated: May 16, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Multiple-platform data integration method with application to combined analysis of microarray and proteomic data.

Shicheng Wu¹, Yawen Xu, Zeny Feng

¹Department of Mathematics and Statistics, York University, 4700 Keele, Street, Toronto, Ontario M3J 1P3, Canada.

BMC Bioinformatics

|December 4, 2012

Summary

This summary is machine-generated.

This study introduces a new method for selecting biomarkers from multi-platform genomic data. The approach improves biomarker discovery by controlling false discovery rates and increasing positive selection rates in disease studies.

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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Published on: August 19, 2025

Area of Science:

Genomics
Biomarker Discovery
Proteomics

Background:

Genomic studies require biomarkers to distinguish between normal and diseased populations using multi-platform data.
Current integration methods often focus on correlation, failing to directly select differentially expressed biomarkers.
Existing methods may not account for dependencies across data platforms.

Purpose of the Study:

To propose a novel integration method for hypothesis testing and biomarker selection using multi-platform data.
To address limitations of existing methods in handling diverse test statistics and dependencies.

Main Methods:

Developed an integration method to aggregate test statistics across platforms using a weighted scheme.
Weights account for varying variabilities of test statistics.
Utilized random permutations to determine the empirical distribution of the aggregated statistic for decision-making.

Main Results:

The proposed method demonstrates superior control over false discovery rates compared to uncombined approaches.
Achieved higher positive selection rates in both simulation and real biological data analyses.
Outperformed rank aggregation methods in terms of statistical power.

Conclusions:

The multi-platform integration method enhances biomarker selection accuracy and reliability.
Offers a more powerful approach for identifying disease-specific biomarkers.
Provides a robust framework for analyzing complex genomic and proteomic datasets.