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Related Concept Videos

Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

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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...
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Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

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Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and reduce chemical noise during analyte detection. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called...
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Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

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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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MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...
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Mass Spectrometry: Overview01:19

Mass Spectrometry: Overview

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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...
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Mass Spectrum: Interpretation01:24

Mass Spectrum: Interpretation

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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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Related Experiment Video

Updated: Apr 26, 2026

Low Molecular Weight Protein Enrichment on Mesoporous Silica Thin Films for Biomarker Discovery
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Derivative component analysis for mass spectral serum proteomic profiles.

Henry Han

    BMC Medical Genomics
    |August 1, 2014
    PubMed
    Summary

    Derivative Component Analysis (DCA) offers a novel machine learning approach for proteomics data, enhancing disease diagnosis by addressing reproducibility issues and improving biomarker discovery for clinical applications.

    Area of Science:

    • Biomedical data analysis
    • Proteomics
    • Machine learning

    Background:

    • Mass spectrometry-based proteomics is crucial for identifying disease biomarkers but lacks effective feature selection methods for clinical diagnosis.
    • Data reproducibility remains a significant challenge, hindering the clinical application of identified proteomic biomarker patterns.

    Purpose of the Study:

    • To introduce Derivative Component Analysis (DCA), a novel machine learning algorithm for high-dimensional mass spectral proteomic profiles.
    • To demonstrate DCA's effectiveness in disease diagnosis and its ability to overcome data reproducibility issues in proteomics.

    Main Methods:

    • Derivative Component Analysis (DCA) is proposed as an implicit feature selection algorithm that uses a multi-resolution approach to capture latent data characteristics and perform de-noising.

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  • DCA is integrated with support vector machines, treating proteomics data as a profile biomarker to address reproducibility challenges and enhance disease diagnosis.

    • Main Results:

    • High-dimensional proteomics data are found to be linearly separable using DCA.
    • DCA effectively overcomes limitations of traditional methods in discovering subtle data behaviors and resolves the reproducibility problem in proteomics data.
    • DCA-based profile biomarker diagnosis achieves reproducible clinical-level diagnostic performance across different proteomic datasets, outperforming existing biomarker discovery methods.

    Conclusions:

    • DCA-based profile biomarker diagnosis is feasible and powerful for high-sensitivity serum proteomics, successfully addressing data reproducibility issues.
    • Subtle data characteristic extraction and de-noising via DCA are critical for separating true signals from noise in high-dimensional proteomic profiles, potentially more so than conventional feature selection.
    • The proposed DCA method and profile biomarker diagnosis are generalizable to other omics data due to DCA's nature as a generic data analysis technique.