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Related Concept Videos

Protein Networks02:26

Protein Networks

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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks02:26

Protein Networks

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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Proteomics01:33

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 proteomics...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...

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Related Experiment Video

Updated: May 10, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

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

Published on: October 19, 2021

Phosphoproteomics-based network medicine.

Zexian Liu1, Yongbo Wang, Yu Xue

  • 1Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China.

The FEBS Journal
|June 12, 2013
PubMed
Summary
This summary is machine-generated.

Phosphoproteomics network medicine identifies complex phospho-signatures for robust cancer biomarkers and drug response prediction. This approach offers more efficient diagnostic and therapeutic targets than single-site analysis.

Keywords:
kinase activityphospho-signaturephosphoproteomicsphosphorylationphosphorylation-mediated signaling network

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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Area of Science:

  • Biochemistry
  • Systems Biology
  • Medical Informatics

Background:

  • Phosphoproteomics aims to identify biomarkers for disease diagnosis and drug targets.
  • Complex diseases involve multiple genes/proteins, necessitating network-based approaches.
  • Single phosphorylation sites yield less robust biomarkers compared to network signatures.

Purpose of the Study:

  • To review the emerging field of phosphoproteomics-based network medicine.
  • To summarize computational methods for reconstructing signaling networks from phosphoproteomic data.
  • To highlight the discovery and application of network phospho-signatures.

Main Methods:

  • Computational reconstruction of phosphorylation-mediated signaling networks.
  • Identification of robust network phospho-signatures.
  • Application of signatures for cancer classification and drug response prediction.

Main Results:

  • Phospho-signatures within networks offer more efficient and robust biomarkers.
  • Systematic network approaches can classify cancers and predict drug responses.
  • Current techniques are evolving but show significant potential.

Conclusions:

  • Phosphoproteomics-based network medicine is a promising field for biomarker discovery.
  • Network phospho-signatures provide superior biomarkers compared to single sites.
  • This systematic approach enhances biomedical applications despite current technical limitations.