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Proteomics

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...

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Statistical Aspects in Proteomic Biomarker Discovery.

¹Department of Medical Statistics, Georg-August-University Göttingen, Humboldtallee 32, 37073, Göttingen, Germany. kjung1@uni-goettingen.de.

Methods in Molecular Biology (Clifton, N.J.)

|November 1, 2015

Summary

Statistical methods are crucial for discovering and validating proteomic biomarkers, essential for personalized medicine. This review covers experimental design, data analysis, and classifier development for proteomic biomarker research.

Keywords:

Classifier Cross-validation Feature selection Molecular signature s Unsupervised learning

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Area of Science:

Biomarker Discovery
Proteomics
Statistical Analysis in Medicine

Background:

Personalized medicine relies on biomarkers for diagnosis, prediction, and prognosis.
Proteomic biomarkers are valuable due to their detectability in tissues and body fluids.
Statistical methods are integral to biomarker discovery and validation.

Purpose of the Study:

To provide an overview of frequent experimental settings for proteomic biomarker studies.
To focus on statistical approaches for exploratory data analysis and classifier development.
To address sample size considerations in proteomic biomarker research.

Main Methods:

Review of statistical methodologies for proteomic biomarker discovery.
Discussion of experimental design and sample size considerations.
Focus on exploratory data analysis and machine learning for classifier development.

Main Results:

Highlights the critical role of statistical methods throughout the proteomic biomarker pipeline.
Emphasizes the importance of appropriate experimental planning and data analysis techniques.
Reviews common approaches for developing predictive models using proteomic data.

Conclusions:

Statistical rigor is essential for reliable proteomic biomarker identification and validation.
Effective application of statistical methods enhances the utility of biomarkers in personalized medicine.
This review offers a guide to statistical considerations in proteomic biomarker research.