Deep Learning Enabled Microarray Gene Expression Classification for Data Science Applications

Abstract

In bioinformatics applications, examination of microarray data has received significant interest to diagnose diseases. Microarray gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of genes. Microarray data classification incorporates multiple disciplines such as bioinformatics, machine learning (ML), data science, and pattern classification. This paper designs an optimal deep neural network based microarray gene expression classification (ODNN-MGEC) model for bioinformatics applications. The proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform scale. Besides, improved fruit fly optimization (IFFO) based feature selection technique is used to reduce the high dimensionality in the biomedical data. Moreover, deep neural network (DNN) model is applied for the classification of microarray gene expression data and the hyperparameter tuning of the DNN model is carried out using the Symbiotic Organisms Search (SOS) algorithm. The utilization of IFFO and SOS algorithms pave the way for accomplishing maximum gene expression classification outcomes. For examining the improved outcomes of the ODNN-MGEC technique, a wide ranging experimental analysis is made against benchmark datasets. The extensive comparison study with recent approaches demonstrates the enhanced outcomes of the ODNN-MGEC technique in terms of different measures.