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Related Concept Videos

Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
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...

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Selecting differentially expressed proteomic markers from mass spectrometry data.

Xuena Wang1, Wei Zhu, James Glimm

  • 1State University of New York, Stony Brook, NY 11794 USA. (phone: 631-632-4408; fax: 631-632-8490;

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 7, 2007
PubMed
Summary

This study introduces novel statistical methods for identifying reliable cancer proteomic markers using high-throughput mass spectrometry. These techniques enhance diagnostic accuracy by distinguishing between cancerous and normal patients and different cancer stages.

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Area of Science:

  • Biomarker Discovery
  • Proteomics
  • Statistical Analysis

Background:

  • High-throughput mass spectrometry is crucial for detecting cancer-related proteomic markers.
  • Accurate identification of these markers is vital for medical diagnostics and cancer staging.

Purpose of the Study:

  • To develop and validate statistical methods for ranking proteomic markers based on their discriminative power.
  • To identify stable cancer markers resilient to training variability using bootstrap resampling.
  • To validate marker patterns using a combined classifier approach.

Main Methods:

  • Application of advanced statistical analysis for marker selection.
  • Utilizing bootstrap resampling to assess marker stability and reduce training variability.
  • Employing a combined classifier for validation of selected marker patterns.
  • Demonstration on a colon cancer dataset analyzed by Surface-Enhanced Laser Desorption/Ionization (SELDI) technology.

Main Results:

  • Proposed statistical methods effectively rank proteomic markers by their separability.
  • Bootstrap strategy identifies stable markers with high classification probability.
  • Validated marker patterns show potential for distinguishing cancer patients from normal individuals and different cancer stages.

Conclusions:

  • The developed statistical methodologies offer a robust approach for identifying reliable cancer proteomic biomarkers.
  • These methods enhance the potential of mass spectrometry in medical diagnostics for cancer detection and classification.
  • The validated marker patterns can significantly aid in the clinical diagnosis and staging of cancer.