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Identification of Biomarkers for Arsenicosis Employing Multiple Kernel Learning Embedded Multiobjective Swarm

Anirban Dey, Kaushik Das Sharma, Tamalika Sanyal

    IEEE Transactions on Nanobioscience
    |July 27, 2022
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    This study identifies key genes linked to arsenic exposure using advanced computational methods. These biomarkers can help predict health risks in populations exposed to arsenic.

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    Area of Science:

    • Environmental Toxicology
    • Bioinformatics
    • Genomics

    Background:

    • Long-term arsenic exposure is a carcinogen, leading to multi-organ disease.
    • Identifying molecular networks of arsenic toxicity is crucial for risk prediction.
    • Biomarker genes are needed to assess arsenic exposure risks.

    Purpose of the Study:

    • To identify candidate biomarker genes from transcriptomic profiles of arsenicosis.
    • To develop a computational framework for classifying arsenic exposure and its subcategories.
    • To understand the molecular networking and toxicity of arsenic.

    Main Methods:

    • A multiple kernel learning (MKL) embedded multi-objective swarm intelligence technique was proposed.
    • Multi-objective random spatial local best particle swarm optimization (MO-RSplbestPSO) was utilized for optimal classification and gene selection.
    • The framework was applied to whole genome DNA microarray data from arsenic-exposed individuals in West Bengal, India.

    Main Results:

    • A set of twelve biomarker genes were successfully identified.
    • Four of the identified genes are novel biomarkers for arsenic exposure.
    • The identified genes enable classification of arsenic exposure and its subcategories.

    Conclusions:

    • The identified twelve biomarker genes, including four novel ones, can be used for screening arsenic-exposed populations.
    • This study provides insights into the complex molecular networking and toxicity mechanisms of arsenic.
    • The developed computational framework offers a robust method for biomarker discovery in environmental toxicology.