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Analysis of Cancer-Associated Mutations of POLB Using Machine Learning and Bioinformatics.

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    Area of Science:

    • Genomics
    • Molecular Biology
    • Bioinformatics

    Background:

    • DNA damage is a key driver of cancer development, necessitating robust DNA repair mechanisms.
    • DNA polymerase beta (Pol β), encoded by the POLB gene, is vital for base excision repair and genome stability.
    • Single Nucleotide Polymorphisms (SNPs) in the POLB gene may alter DNA repair efficiency and influence cancer susceptibility.

    Purpose of the Study:

    • To investigate the association between POLB gene variations (SNPs) and cancer risk.
    • To evaluate the utility of machine learning algorithms in predicting cancer likelihood based on POLB SNPs.
    • To explore the role of Pol β in DNA repair and its implications for cancer onset.

    Main Methods:

    • Bioinformatics tools were employed to extract features from POLB gene SNPs.
    • A feature matrix was constructed using these extracted SNP features.
    • Eight distinct machine learning algorithms were applied to predict cancer risk associated with POLB variations.

    Main Results:

    • The study successfully identified relationships between specific POLB gene SNPs and cancer development.
    • Machine learning models demonstrated effectiveness in predicting cancer likelihood based on POLB genetic variations.
    • The findings highlight the complex interplay between POLB gene SNPs, DNA repair, and cancer.

    Conclusions:

    • POLB gene variations are implicated in cancer onset and progression.
    • Machine learning offers a powerful approach for analyzing genomic data and predicting cancer risk from genetic variations.
    • This research provides a foundation for developing advanced predictive models in cancer genomics.