Microarray Data Classification and Gene Selection Using Convolutional Neural Network

Over the past years, there is a rapid expansion for handling bioinformatics data, particularly processing with gene expression levels via microarrays. Due to the characteristic of microarray data, which often entails with more features and less samples, the task of classifying this data becomes notably intricate. By using microarray technology, gene expression profiles may be produced in massive quantities. Currently, gene expression data are used to diagnose illness. The use of deep learning algorithms is one such method that aids in this process. These methods work well for classifying and identifying informative genes. The classes of testing samples may be predicted using these genes. Microarray data used to identify cancer often has a small number of samples and a large feature collection size derived from gene expression data. Use of deep learning algorithms is currently receiving a lot of interest in the field of artificial intelligence to address various problems. In this paper, we examined a deep learning system for microarray categorization based on the convolutional neural network (CNN) over other machine learning techniques. The effectiveness of CNN has been compared with existing system, and results are discussed.