Abstract
In the dynamic realm of molecular biology, the comprehension and prediction of gene promoter sequences stand as linchpins for unravelling the intricacies of genetic regulation. This research undertakes a comprehensive study, aiming to forge a robust system for the classification of gene promoter sequences. Harnessing the capabilities of advanced machine learning algorithms, our proposed system endeavours to precisely categorize these sequences into distinct classes, thus laying the groundwork for enhanced gene expression analysis and the identification of regulatory elements. At the core of our approach lies the recognition that accurate classification of gene promoter sequences is pivotal for unlocking a deeper understanding of genetic regulation. By leveraging the sophistication of machine learning, we not only strive to improve the efficiency of classification but also contribute to a more nuanced exploration of the underlying mechanisms governing gene activation and repression. The proposed system emerges as a transformative tool, offering researchers a precise lens through which to decipher the complexities of genetic information, fostering advancements in molecular biology and genomics.
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