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Updated: Mar 12, 2026

MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as a Novel Detection and Quantification Method
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MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as a Novel Detection and Quantification Method

Published on: October 7, 2025

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Fuzzy-Rough Entropy Measure and Histogram Based Patient Selection for miRNA Ranking in Cancer.

Jayanta Kumar Pal, Shubhra Sankar Ray, Sung-Bae Cho

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |November 11, 2016
    PubMed
    Summary
    This summary is machine-generated.

    A new Fuzzy-Rough Entropy Measure (FREM) effectively identifies cancer-indicating microRNAs (miRNAs) by ranking their relevance. This method enhances cancer detection accuracy and reduces patient data for analysis.

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    Area of Science:

    • Biomedical Informatics
    • Computational Biology
    • Cancer Research

    Background:

    • MicroRNAs (miRNAs) are crucial biomarkers for cancer detection.
    • Identifying specific miRNAs is key to diagnosing cancer.
    • Existing methods for miRNA relevance assessment have limitations.

    Purpose of the Study:

    • To develop a novel Fuzzy-Rough Entropy Measure (FREM) for ranking and identifying cancer-relevant miRNAs.
    • To enhance the accuracy and efficiency of miRNA-based cancer detection.
    • To assess the redundancy of selected miRNAs and reduce patient data for computation.

    Main Methods:

    • Developed a Fuzzy-Rough Entropy Measure (FREM) to quantify miRNA relevance.
    • Utilized fuzziness to handle overlapping expression data between normal and cancer cells.
    • Employed rough lower approximation to determine class sizes for miRNA expression.
    • Implemented a histogram-based patient selection method to optimize computational load.
    • Removed redundant miRNAs to refine the selected set.

    Main Results:

    • FREM successfully ranks miRNAs based on their class separability.
    • Selected miRNAs demonstrated high relevance, with scores ranging from 0.70 to 0.91 using SVM.
    • FREM outperformed existing methods, achieving scores from 0.37 to 0.90 on benchmark datasets.
    • The identified miRNAs were validated through biological investigations and pathway analysis.
    • The histogram-based method minimally impacted performance while reducing patient numbers.

    Conclusions:

    • FREM is a superior method for identifying cancer-relevant miRNAs.
    • The developed approach improves sensitivity and specificity in cancer detection.
    • FREM offers a computationally efficient and biologically relevant tool for cancer biomarker discovery.