What is it about?
research article brings into focus the advance application of Grey Wolf Optimizer (CNN-GWO) which is used to differentiate genes correlated with breast cancer. This innovative approach employs the GSE45827 dataset to identify genetic patterns which may exhibit specific molecular signatures associated with breast cancer and therefore potentially benefit patients with individualized treatment options. The use of CNNs (convolution neural networks) and GWO (Grey Wolf Optimizer) raised not just the efficiency of learning complex genetic patterns, but also led to a much better understanding of genetic association and molecular pathway. This innovative approach promises to bring novel avenues towards the identification of therapeutic targets and prognostic markers as well as to deepen the knowledge about breast cancer at the genetic level. After illustrates the performance of both CNN and CGWO algorithms was evaluated. The efficacy of the GWO approach for selecting 'Random seed' values in CNN models was evaluated, demonstrating high accuracy (0.97561) according to the results.
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Why is it important?
CNN models fine-tuned with GWO demonstrated improved generalization and increased stability across multiple datasets. The adaptability of these models was bolstered by the versatility of the 'Training dataset' values, diminishing the risk of overfitting or under fitting and ensuring robust performance in diverse scenarios
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This page is a summary of: Unveiling the potential of the grey wolf optimized - Based convolutional neural networks algorithm for accurate breast cancer detection, January 2025, American Institute of Physics,
DOI: 10.1063/5.0264930.
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