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Related Concept Videos

Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
High-Resolution Mass Spectrometry (HRMS)01:15

High-Resolution Mass Spectrometry (HRMS)

The resolution of a mass spectrometer depends on the efficiency of separating ions with different ion masses. The mass of an atom is approximated to the sum of the masses of protons and neutrons inside, considering the masses of protons and neutrons as equal. However, the masses of the proton (1.6726 × 10−24 g) and neutron (1.6749 × 10−24 g) are not truly equal. There is a minor error in the expression of atomic masses relative to the simplest atom of hydrogen. For example, the mass of helium...
Mass Spectrometry: Overview01:19

Mass Spectrometry: Overview

Mass spectrometry is an analytical technique used to determine the molecular mass and molecular formula of a compound. The basic principle of mass spectrometry is to generate ions from the analyte molecule and measure these ion abundances against their molecular mass. One common type of ionization, known as electron ionization or EI, bombards the analyte molecules in the gas phase with high-energy electron beams. The electron beams displace an electron from the molecule and leave behind a...
Mass Spectrometry: Branched Alkane Fragmentation01:29

Mass Spectrometry: Branched Alkane Fragmentation

This lesson delves into the mass spectrometry of branched alkane fragmentation. Branched alkanes possess secondary or tertiary carbon atoms, which generate relatively stable carbocations if the cleavage occurs at the branching point. The high stability of carbocations drives the instant fragmentation of branched alkanes. Accordingly, the branched alkane's molecular ion peak is very weak or invisible in the mass spectra, especially in comparison to a linear alkane.
Mass Spectrometry: Molecular Fragmentation Overview01:20

Mass Spectrometry: Molecular Fragmentation Overview

The ionization of a molecule into a molecular ion inside the mass spectrometer causes instability in the molecule's structure due to the loss of an electron. This eventually leads to the fragmentation or breaking of some bonds in the molecule. The fragmentation occurs predominantly at specific bonds to yield relatively stable fragments.
One type of fragmentation pattern is the cleavage of a single bond in the molecular ion. The cleavage leads to a radical and a cation. The cleavage can occur at...
Mass Spectrum: Interpretation01:24

Mass Spectrum: Interpretation

An unknown compound can be established by identifying the molecular ion peak in the mass spectrum. The molecular ion peak is often weak or absent due to the predominance of fragmentation in high-energy electron beams. In such cases, a soft-energy electron beam can be used to scan the spectrum to enhance the intensity of the molecular ion peak. Additionally, chemical ionization, field ionization, and desorption ionization spectra are used to obtain a relatively intense molecular ion peak.To...

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Updated: Jun 14, 2026

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products
11:13

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

Published on: March 12, 2020

A Bayesian network approach to feature selection in mass spectrometry data.

Karl W Kuschner1, Dariya I Malyarenko, William E Cooke

  • 1Department of Physics, The College of William and Mary, Williamsburg, VA, USA. kwkusc@wm.edu

BMC Bioinformatics
|April 10, 2010
PubMed
Summary
This summary is machine-generated.

A new Bayesian method enhances cancer detection using time-of-flight mass spectrometry (TOF-MS) by identifying stable protein biomarkers. This approach overcomes data variability and overfitting for reliable disease screening.

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Semi-Quantitative Analysis of Peptidoglycan by Liquid Chromatography Mass Spectrometry and Bioinformatics
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Last Updated: Jun 14, 2026

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products
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Semi-Quantitative Analysis of Peptidoglycan by Liquid Chromatography Mass Spectrometry and Bioinformatics
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Semi-Quantitative Analysis of Peptidoglycan by Liquid Chromatography Mass Spectrometry and Bioinformatics

Published on: October 13, 2020

Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Analytical Chemistry

Background:

  • Time-of-flight mass spectrometry (TOF-MS) offers potential for non-invasive disease screening via protein biomarker detection.
  • High measurement variability and statistical challenges hinder the realization of TOF-MS diagnostic potential.
  • Overfitting is a significant complication in analyzing limited patient data with numerous variables.

Purpose of the Study:

  • To develop a robust Bayesian method for analyzing TOF-MS data.
  • To overcome limitations of current statistical tools in identifying reliable diagnostic markers.
  • To create a stable classifier for disease categorization with accurate error prediction.

Main Methods:

  • Developed a Bayesian inductive method using model-independent approaches to uncover spectral feature relationships.
  • Applied the method to artificial data with simulated TOF-MS variability.
  • Utilized the method on blood sera data from a leukemia study.

Main Results:

  • The Bayesian method accurately recovered known feature relationships in artificial data.
  • High stability of selected features was observed in the leukemia dataset via cross-validation.
  • The method demonstrated excellent predictive power on withheld data and outperformed traditional techniques.
  • Identified a potential protein biomarker consistent with existing cancer research and experimentally verified.

Conclusions:

  • The developed Bayesian method effectively avoids overfitting in biological data analysis.
  • It produces stable feature sets organized in a network model, aiding biochemical analysis.
  • The method yields more consistent feature sets for classifying new data compared to traditional techniques.