Md. Inzamam-Ul-Hossain
ABSTRACT
Essential proteins play a vital role in survival as well as the reproduction of an organism. It can be identified by the proteinprotein interaction (PPI) network. By the characteristics of the interaction network, we can differentiate essential protein from non-essential ones. It is necessary to understand the PPI for getting knowledge of protein functions and the information they carry. With the knowledge of essential proteins, it is possible to identify disease, lethal proteins, and design drugs like antibiotics. As experimental methods are time-consuming, error-prone and laborious, the computational approach becomes popular to predict essential proteins. Many kinds of researches have been done to identify essential proteins. Among them, machine learning techniques are found more promising. In this paper, we have proposed a method to identify essential protein using eight features calculated from the PPI network. We prepare dataset and classify it with three classifiers named XGBoost (eXtreme Gradient Boosting) Tree, C 5.0 and an ensemble method that includes two classifiers (XGBoost Tree and C 5.0). After evaluating results, we found that C 5.0 outperforms the results of XGBoost Tree and ensemble method. We also compare the results with other existing methods and get a very good improvement over the others.
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Index Terms
- An Efficient Approach for Prediction of Essential Protein from PPI Network
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