Microarray gene expression classification: dwarf mongoose optimization with deep learning

Abstract

The deoxyribonucleic acid (DNA) microarray model holds significant promise for revealing expression data from thousands of genes. It serves as a valuable tool for investigating gene expressions in diverse biological research fields. This study explores advancements in gene selection for cancer detection through artificial intelligence, with a focus on the challenge of extracting pertinent information from vast databases. The application of deep learning architecture in detecting chronic diseases and aiding medical decision-making has proven effective across various domains. Therefore, this study designs an enhanced microarray gene expression classification by utilizing a dwarf mongoose optimization with deep learning (MGEXC-DMODL) approach. The MGEXC-DMODL approach intends to classify the microarray gene expression (MGE). For this, the MGEXC-DMODL technique initially applies the wiener filtering (WF) technique to eradicate the noise. In addition, the MGEXC-DMODL technique employs a deep residual shrinkage network (DRSN) to learn feature vectors. Meanwhile, the convolutional autoencoder (CAE) model was executed for identifying and classifying the MGE data. Furthermore, the dwarf mongoose optimization (DMO)-based hyperparameter tuning is performed to enhance the detection outcomes of the CAE model. The investigational evaluation of the MGEXC-DMODL model is validated using a benchmark database. The comprehensive comparison outcome highlighted the betterment of the MGEXC-DMODL model over recent approaches.