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Computational investigation identifies pathogenic variants in TP53 gene, highlighting its role in cancer mechanisms.
This study presents a computational investigation of TP53 gene mutations using bioinformatics tools. Mutation datasets were analyzed to identify pathogenic variants and predict their structural and functional impact. The study integrates sequence analysis, mutation annotation, and data visualization approaches implemented in Python. The findings contribute to understanding mutation-driven oncogenic mechanisms and demonstrate the application of computational biology in cancer research.
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pushkar barsagade (2026) studied this question.
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