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Updated: Feb 20, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
Published on: November 15, 2017
Proteomics and phosphoproteomics in precision medicine: applications and challenges
Girolamo Giudice1, Evangelia Petsalaki1
1European Molecular Biology Laboratory European Bioinformatics Institute.
Proteomics enables precise molecular mechanism identification for personalized medicine. This review highlights successful applications, bioinformatics challenges, and strategies for integrating proteomics data into clinical settings for improved patient care.
Area of Science:
- Biochemistry
- Molecular Biology
- Bioinformatics
Background:
- Proteomics and phosphoproteomics now allow accurate measurement of thousands of proteins and phosphoproteins across multiple samples.
- This technological advance presents an unprecedented opportunity for systematic analysis of patient samples and disease models.
Purpose of the Study:
- To review successful applications of proteomics and phosphoproteomics data in precision medicine.
- To discuss the integration of proteomics with other omics data for enhanced precision medicine strategies.
- To identify bioinformatics challenges and barriers to clinical adoption of proteomics biomarkers.
Main Methods:
- Review of existing literature on proteomics and phosphoproteomics applications in precision medicine.
- Analysis of case studies demonstrating successful data integration.
- Discussion of bioinformatics tools and workflows for large-scale omics data analysis.
Main Results:
- Proteomics data has been successfully used to identify molecular mechanisms, discover personalized biomarkers, and inform treatment strategies.
- Integration with other omics data enhances the discovery potential of proteomics.
- Significant bioinformatics challenges exist in data generation, analysis, and integration.
Conclusions:
- Proteomics holds immense potential for advancing precision medicine and clinical diagnostics.
- Overcoming bioinformatics hurdles and demonstrating clinical utility are key to wider adoption.
- Future efforts should focus on standardizing data analysis and facilitating clinical translation of proteomics findings.
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