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Related Concept Videos

Cancer-Critical Genes I: Proto-oncogenes01:33

Cancer-Critical Genes I: Proto-oncogenes

Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...
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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.
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Cancer-Critical Genes II: Tumor Suppressor Genes01:05

Cancer-Critical Genes II: Tumor Suppressor Genes

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MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

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Related Experiment Video

Updated: Jul 1, 2026

Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors
08:08

Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors

Published on: February 27, 2015

Cancer informatics by prototype networks in mass spectrometry.

Frank-Michael Schleif1, Thomas Villmann, Markus Kostrzewa

  • 1University Leipzig, Department of Medicine, Computational Intelligence Group, Semmelweisstrasse 10, 04103 Leipzig, Germany. schleif@informatik.uni-leipzig.de

Artificial Intelligence in Medicine
|September 10, 2008
PubMed
Summary

This study introduces a new method using functional data analysis and conformal prediction to classify clinical proteomic spectra. The approach enhances biomarker discovery and provides confidence measures for cancer research classifications.

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Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
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Quantification of Proteins Using Peptide Immunoaffinity Enrichment Coupled with Mass Spectrometry
06:09

Quantification of Proteins Using Peptide Immunoaffinity Enrichment Coupled with Mass Spectrometry

Published on: July 31, 2011

Area of Science:

  • Biochemistry
  • Computational Biology
  • Oncology

Background:

  • Mass spectrometry is crucial for analyzing clinical samples in cancer research, yielding high-dimensional proteomic data.
  • Classical statistical methods face limitations in clinical proteomics due to small sample sizes and high data dimensionality.
  • Existing machine learning models often lack reliable confidence measures for their classification decisions.

Purpose of the Study:

  • To develop a robust classification method for clinical proteomic spectra using functional data analysis.
  • To integrate conformal prediction for reliable confidence measures in classification decisions.
  • To identify potential cancer biomarker candidates from mass spectrometry data.

Main Methods:

  • Wavelet-based techniques were employed for efficient processing and encoding of mass spectrometric measurements.
  • A prototype-based classifier was extended with a functional metric and combined with conformal prediction.
  • The methodology was applied to classify clinical proteomic spectra from colorectal and lung cancer studies.

Main Results:

  • The proposed algorithm demonstrated effectiveness in analyzing functional data from clinical mass spectrometry.
  • Evaluation focused on prediction accuracy and confidence of classification decisions for prototype classifiers.
  • Analysis of metric parameters facilitated the identification of potential biomarker candidates.

Conclusions:

  • The developed algorithm provides a robust framework for analyzing functional data in clinical mass spectrometry.
  • It enables the identification of discriminating mass positions and assessment of classification confidence.
  • The approach yields interpretable classification models crucial for cancer research.