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Related Concept Videos

FISH - Fluorescent In-situ Hybridization02:07

FISH - Fluorescent In-situ Hybridization

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Fluorescence in situ hybridization, or FISH, was developed in the early 1980s and has quickly become one of the most widely used techniques in cytogenetics. Labeled probes are used to bind complementary DNA or RNA sequences on a chromosome or in a region within a cell. Earlier, the probes could only be obtained by cloning or reverse transcription of a DNA template. Currently, the probe oligonucleotides can be synthesized synthetically. Additionally, with the advancement of optical techniques,...
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In 1882, Flemming observed lampbrush chromosomes (LBC) in salamander eggs. Later in 1892, Rückert observed LBCs in shark egg cells and coined the term "lampbrush chromosomes" because they looked like brushes used to clean kerosene lamps.
LBCs are made up of two pairs of conjugating homologous chromatids. Each chromatid consists of alternatively positioned regions of condensed-inactive chromatin and loosely placed-active side loops, which can be contracted and extended. The loops...
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Robust 3D DNA FISH Using Directly Labeled Probes
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Fully unsupervised M-FISH chromosome image characterization.

Petros S Karvelis, Aristidis C Likas

    IEEE Journal of Biomedical and Health Informatics
    |November 19, 2013
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an unsupervised method for classifying multiplex fluorescent in situ hybridization (M-FISH) chromosome images. The novel approach achieves high accuracy without needing annotated training data, simplifying chromosome analysis.

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    Area of Science:

    • Cytogenetics
    • Bioinformatics
    • Medical Imaging

    Background:

    • Chromosome analysis is crucial for clinical diagnosis and biological research.
    • Multiplex fluorescent in situ hybridization (M-FISH) aids chromosome analysis by assigning unique colors to each chromosome class.
    • Image variations in M-FISH, due to uneven hybridization and spectral overlap, challenge supervised classification methods.

    Purpose of the Study:

    • To develop a fully unsupervised methodology for M-FISH chromosome image classification.
    • To overcome limitations of supervised methods that require annotated training data.
    • To improve the accuracy and convenience of M-FISH image analysis.

    Main Methods:

    • Utilizing a Gaussian mixture model (GMM) where each component represents a chromosome class based on pixel intensity.
    • Initializing the GMM using prior knowledge of chromosome class emissions across M-FISH channels.
    • Applying the unsupervised classification to a large dataset of M-FISH images.

    Main Results:

    • Achieved an overall classification accuracy of 89.85% on M-FISH images.
    • Demonstrated superior classification accuracy compared to common supervised methods.
    • Validated the effectiveness of the unsupervised approach in scenarios lacking ground truth data.

    Conclusions:

    • The proposed unsupervised GMM-based method offers a robust and accurate solution for M-FISH chromosome image classification.
    • This technique eliminates the need for training data, making it highly practical for clinical and research applications.
    • The method enhances the reliability and efficiency of chromosome analysis, particularly when ground truth is unavailable.