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Identifying Cancer Biomarkers From Microarray Data Using Feature Selection and Semisupervised Learning.

Debasis Chakraborty, Ujjwal Maulik

    IEEE Journal of Translational Engineering in Health and Medicine
    |May 13, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel method combining kernelized fuzzy rough sets (KFRS) and semisupervised support vector machines (S3VM) to predict cancer biomarkers from microRNA and gene expression data, addressing small sample size limitations.

    Keywords:
    Cancer biomarkersfeature selectionkernelized fuzzy rough setmicroarray datasemisupervised SVMsuccessive filtering

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    Area of Science:

    • Genomics and Bioinformatics
    • Cancer Research
    • Computational Biology

    Background:

    • Microarray analysis is crucial for understanding gene and micro-RNA (miRNA) expression profiles in various biological contexts.
    • Altered expression patterns in cancer serve as potential biomarkers for diagnosis, prognosis, and treatment prediction.
    • Small sample sizes in microarray data pose challenges for developing robust classification and biomarker discovery methods.

    Purpose of the Study:

    • To propose a novel computational approach for identifying cancer biomarkers from miRNA and gene expression data.
    • To address the challenge of small sample sizes in microarray datasets for biomarker prediction.
    • To evaluate the effectiveness of the proposed method in discovering biologically significant cancer biomarkers.

    Main Methods:

    • A hybrid approach combining kernelized fuzzy rough set (KFRS) for feature selection and semisupervised support vector machine (S3VM) for classification was developed.
    • Three feature selection methods, including KFRS, were employed to identify potential cancer biomarkers.
    • The proposed KFRS and S3VM combination was applied to miRNA and gene expression microarray datasets.

    Main Results:

    • The effectiveness of the combined KFRS and S3VM approach was demonstrated on multiple microarray datasets.
    • Cancer biomarkers were successfully identified from both miRNA and gene expression profiles.
    • Biological significance tests confirmed the relevance of the identified miRNA cancer biomarkers.

    Conclusions:

    • The proposed KFRS and S3VM method offers a promising strategy for cancer biomarker discovery from microarray data, particularly in scenarios with limited sample sizes.
    • The identified miRNA biomarkers hold potential for applications in cancer diagnosis and prognosis.
    • This study highlights the utility of integrating advanced machine learning techniques with high-throughput expression profiling for cancer research.