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Related Concept Videos

Proteomics01:33

Proteomics

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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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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Related Experiment Video

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Feature Selection Methods for Protein Biomarker Discovery from Proteomics or Multiomics Data.

Zhiao Shi1, Bo Wen1, Qiang Gao2

  • 1Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.

Molecular & Cellular Proteomics : MCP
|April 22, 2021
PubMed
Summary

This study introduces ProMS, a computational algorithm for selecting protein biomarkers from mass spectrometry (MS) proteomics data. ProMS identifies functional protein clusters and representative markers, improving biomarker discovery and validation for clinical translation.

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Area of Science:

  • Biomarker Discovery
  • Proteomics
  • Computational Biology

Background:

  • Clinical translation of protein biomarkers from untargeted mass spectrometry (MS)-based proteomics is hindered by challenges in selecting generalizable and platform-adaptable markers.
  • Existing methods lack approaches for multiomics-facilitated protein biomarker selection, despite the increasing use of multiomics data in discovery studies.

Purpose of the Study:

  • To develop a computational algorithm, ProMS, for effective protein marker selection from proteomics and multiomics data.
  • To enable functional interpretation and facilitate robust transition of selected protein markers to verification and validation platforms.

Main Methods:

  • ProMS utilizes a weighted k-medoids clustering algorithm on univariately informative proteins to identify coexpressed protein clusters and representative markers.
  • An extension, ProMS_mo, employs constrained weighted k-medoids clustering for multiomics data integration.
  • The algorithm is evaluated on two clinically relevant classification problems.

Main Results:

  • ProMS demonstrates superior performance compared to existing feature selection methods in identifying protein markers.
  • ProMS_mo, using multiomics data, shows improved performance on independent test data compared to ProMS.
  • The identified protein clusters offer functional insights and facilitate the selection of alternative markers.

Conclusions:

  • ProMS and ProMS_mo provide a unified and effective computational framework for protein biomarker selection.
  • The algorithms enhance the reliability and interpretability of protein biomarkers for clinical applications.
  • The software is publicly available, promoting broader adoption in proteomics and multiomics research.