<![CDATA[The interaction between genetic and environmental factors plays a crucial role in determining phenotypic traits in organisms. This study aims to analyze these interactions using computational approaches, including statistical models and machine learning algorithms. The data used include genetic factors (genotypes) and simulated environmental factors. Results indicate that machine learning models such as Random Forest can detect interaction patterns with high accuracy, as demonstrated by significant R² values. Additionally, heatmap visualizations provide deeper insights into the non-linear effects of genetic-environment interactions. This study highlights the potential of computational methods in exploring complex interactions, with broad applications in health, agriculture, and biotechnology.]]>
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