Efficient Gene Expression Data Analysis using ES-DBN For Microarray Cancer Data Classification

INTRODUCTION: DNA microarray has become a promising means for classification of various cancer types via the creation of various Gene Expression (GE) profiles, with the advancement of technologies. But, it is challenging to classify the GE profile since not all genes contribute to the presence of cancer and might lead to incorrect diagnoses. Thus an efficient GE data analysis for microarray cancer data classification using Exponential Sigmoid-Deep Belief Network (ES-DBN) is proposed in this work. OBJECTIVES: The study aims to develop an efficient GE data analysis using Exponential Sigmoid-Deep Belief Network (ES-DBN) for microarray cancer data classification. METHODS: The proposed methodology starts with pre-processing to compact data. Afterward, by utilizing Min-Max feature scaling technique, the pre-processed data is normalized. The normalized data is further encoded and feature  ranking is performed. The subset values are selected using Cauchy Mutation-Coral Reefs Optimization (CM-CRO) in feature ranking. The feature vector is calculated by Pearson Correlation Coefficient based GloVe (PCC-GloVe) algorithm since different subsets return the same fitness value. Statistical and Biological validations take place after feature vector calculation. Lastly, for effective classification of the type of cancer, the vector features obtained are fed to ES-DBN. RESULTS: The outcomes of the proposed technique are evaluated with various datasets, which exhibited that the proposed technique performed well with the Ovarian cancer dataset and outperforms other conventional approaches. CONCLUSION: This study presents a comprehensive methodology for efficiently classifying cancer types using GE profile. The proposed GE data analysis using ES-DBN shows promising results, highlighting its potential as a valuable tool for cancer diagnosis and classification.