Performance Evaluation of Hybrid Bio-Inspired and Deep Learning Algorithms in Gene Selection and Cancer Classification

Cancer classification based on gene expression data is a critical challenge in modern bioinformatics, requiring efficient and accurate feature selection methods. This study explores the performance of hybrid bio-inspired algorithms and deep learning techniques for gene selection and cancer classification. Hybrid bio-inspired methods, inspired by natural optimization processes, have demonstrated significant advantages in navigating high-dimensional genomic data. Meanwhile, deep learning models excel in pattern recognition and automated feature extraction, offering a complementary approach to traditional gene selection techniques. This paper systematically evaluates both approaches, highlighting their strengths and limitations in terms of classification accuracy, computational efficiency, and feature selection effectiveness. Our findings reveal that hybrid bio-inspired methods, such as Grey Wolf Optimizer and Harris Hawks Optimization, achieve high classification accuracy with minimal selected genes, making them computationally efficient for clinical applications. Conversely, deep learning models, including convolutional neural networks and autoencoders, demonstrate superior feature extraction but often require larger datasets and higher computational resources. By providing a comparative analysis, this study aims to guide researchers and clinicians in selecting the most suitable approach for cancer classification tasks. The results underscore the potential of hybrid methodologies in advancing precision oncology while identifying opportunities for future improvements in deep learning-based feature selection.