LAUSR

For the survival, development, and reproduction of the organism, understanding the working process of the cell, disease study, design drugs, etc. essential protein plays a crucial role. Due to a large number of biological information, computational methods are becoming popular in recent times to identify the essential protein. Many computational methods used machine learning techniques, metaheuristic algorithms, etc. to solve the problem. The problem with these methods is that the essential protein class prediction rate is still low. Many of these methods have not considered the imbalance characteristics of the dataset. In this paper, we have proposed an approach to identify essential proteins using a metaheuristic algorithm named Chemical Reaction Optimization (CRO) and machine learning method. Both topological and biological features are used here. The Saccharomyces cerevisiae (S. cerevisiae) and Escherichia coli (E. coli) datasets are used in the experiment. Topological features are calculated from the PPI network data. Composite features are calculated from the collected features. Synthetic Minority Over-sampling Technique and Edited Nearest Neighbor (SMOTE+ENN) technique is applied to balance the dataset and then the CRO algorithm is applied to achieve the optimal number of features. Our experiment shows that the proposed approach gives better results in both accuracy and f-measure than the existing related methods.