Deep Learning Enabled Microarray Gene Expression Classification for Data Science Applications

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