Integrating Chicken Swarm Optimization with Deep Learning for Microarray Gene Expression Classification

Microarray Gene Expression classification is a computational model that is vital in interpreting biological data encoded in profiles of gene expression. Using microarray technology, which permits synchronized measurement of numerous genes, this classification model proposes to classify samples into different clusters dependent upon their gene expression patterns. By examining the massive amount of data produced from microarray experimentations, researchers can discover significant insights into numerous biological procedures, classify possible biomarkers for diseases, and improve understanding of cellular devices. The procedure includes preprocessing raw data, removing appropriate features, and using sophisticated classification methods to precisely allocate samples to exact pathological or phenotypic types, donating considerably to the areas of biomedical and genomics research. In this study, a Microarray Gene Expression Classification using Chicken Swarm Optimization with Deep Learning (MGEC-CSODL) model is presented, intended to optimize the accuracy and efficacy of gene expression classification. The technique combines adaptive histogram-based preprocessing to enhance data representation, uses DenseNet201 as an influential feature extraction for strong feature learning, employs Chicken Swarm Optimizer (CSO) for hyperparameter fine-tuning to improve model performance, and includes a Graph Convolutional Network (GCN) for precise classification of gene expression. The experimental outcomes establish the efficacy of the MGEC-CSODL model, showcasing important growths in classification accuracy when equated to present models. This state-of-the-art technique not only progresses the area of bioinformatics but also delivers an effective tool for precise and effectual analysis of gene expression in the era of high-throughput genomics.