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Related Concept Videos

Classification of Epithelial Tissues: Overview01:22

Classification of Epithelial Tissues: Overview

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Epithelial tissues are classified according to the shape of the cells and the number of cell layers formed. Cell shapes can be squamous (flattened and thin), cuboidal (square-like, as wide as it is tall), or columnar (rectangular, taller than it is wide). Additionally, the nucleus shape helps identify the type of epithelial cells. Squamous cells have flattened disc-shaped nuclei, cuboidal cells have spherical nuclei, and columnar cells have elongated nuclei.
Based on the number of cell layers,...
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Classification of Epithelial Tissues: Stratified Epithelium01:29

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Stratified epithelium consists of several stacked layers of cells. They provide the durability to withstand constant physical and chemical attacks. Stratified epithelium is named after the shape of the most apical layer of cells. Stratified squamous epithelium is the most common type found in the human body. In this tissue, the apical cells are squamous, whereas the basal layer contains either columnar or cuboidal cells. The basal cells divide to form new daughter cells, which gradually become...
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Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
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Classification of serous ovarian tumors based on microarray data using multicategory support vector machines.

Jee Soo Park, Soo Beom Choi, Jai Won Chung

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 9, 2015
    PubMed
    Summary

    Machine learning accurately classifies ovarian cancer subtypes, distinguishing normal cells, borderline tumors, and carcinomas. This approach identifies key biomarkers for targeted therapies.

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    Area of Science:

    • Oncology
    • Bioinformatics
    • Machine Learning

    Background:

    • Ovarian cancer is a leading cause of cancer death in women.
    • Serous borderline ovarian tumors (SBOTs) represent an earlier stage of serous ovarian carcinomas (SOCs).
    • SBOTs are often asymptomatic, leading to common progression to advanced stages.

    Purpose of the Study:

    • To develop and optimize multicategory classification models for discriminating ovarian cancer subclasses using DNA microarray data.
    • To evaluate machine learning algorithms and feature selection methods for accurate subclass identification.

    Main Methods:

    • Utilized DNA microarray data from 113 subjects (22 normal, 12 SBOTs, 79 SOCs).
    • Systematically evaluated three machine learning algorithms and three feature selection methods.
    • Employed five-fold cross-validation and grid search for model optimization.
    • Applied support vector machines one-versus-rest with signal-to-noise feature selection.

    Main Results:

    • Achieved 97.3% accuracy, 0.916 relative classifier information, and 0.941 kappa index with the optimal model.
    • Identified five key features, including SNTN and AOX1 expression, for differentiating between normal, SBOT, and SOC groups.
    • Demonstrated the effectiveness of machine learning in classifying ovarian tumor subclasses.

    Conclusions:

    • Multicategory machine learning provides a cost-effective and simple method for accurate ovarian tumor subclass diagnosis.
    • Identified putative biomarkers (SNTN, AOX1) for improved differentiation.
    • Enables more effective subclass-targeted therapy for ovarian cancer patients.