
Prediction of Essential Protein Using Machine Learning Technique
Category:- Journal; Year:- 2021
Discipline:- Computer Science & Engineering Discipline
School:- Science, Engineering & Technology School
Abstract
For the survival and reproduction of organisms, essential
proteins are crucial. Identification of the essential protein is important for
cell working and drug design. Essential proteins are predicted from many
protein-protein interactions (PPI) networks that are developed using
high-throughput techniques. Computational methods are used by many existing
proposed techniques to identify essential proteins. Many of them considered
topological features for essential protein prediction. Some of the research
works consider both topological and biological features to identify essential
proteins. In this paper, we have proposed a method using machine learning
techniques to accomplish the purpose. Here the Saccharomyces Cerevisiae dataset
is considered for essential protein prediction. Three classifiers such as
XGBoost, Random Forest, and decision tree have been used to predict the
essential proteins. We also apply ensemble methods combined with the three
classifiers XGBoost, Random Forest, and decision tree for essential protein
prediction. The ensemble method gives the highest accuracy to identify
essential proteins compared with the other existing methods. On the other hand,
XGBoost gives the highest F1 score.