Enhancing Healthcare Through Metaheuristic Based Deep Learning: Microarray Gene Expression Image Classification Model

Abstract

Microarray gene expression data analysis has revolutionized genomics by providing insights into the genetic basis of diseases, including cancer. However, the high dimensionality of microarray data poses significant challenges for accurate classification, often leading to overfitting and poor generalization. This study proposes a novel framework that integrates the DNA Bidirectional Encoder Representations from Transformers (DNABERT) with the Mayfly Optimization Algorithm (MFO) to improve the classification accuracy of microarray gene expression data. DNABERT, a transformer-based model pretrained on genomic sequences, excels in learning complex gene interactions through bidirectional contextual embeddings. MFO, a bio-inspired optimization technique, addresses feature selection and hyperparameter tuning by balancing exploration and exploitation in high-dimensional search spaces. The framework, DNABERT-MFO, leverages MFO to identify the most relevant gene subsets and optimize DNABERT's hyperparameters, improving the model’s performance. Evaluations conducted on multiple microarray datasets, demonstrate that DNABERT-MFO with overall accuracy of above 80%  significantly outperforms traditional machine learning methods and standalone deep learning models in classification accuracy. This integrated method not only enhances the robustness of gene expression data analysis but also offers a powerful tool for research and clinical applications in genomics. The proposed method addresses key limitations of existing techniques and provides a promising avenue for future advancements in gene expression classification.