Ensemble Learning based Enzyme Family Classification using n-gram Feature

The human body has a huge variety of enzymes, each of which contributes to the acceleration of a certain kind of biochemical reaction. The majority of metabolic processes in living organisms move more slowly at moderate temperatures and pressures when these proteins are absent from the equation. Because of the strong relationship between the newly found enzyme molecule and the particular target on which it acts, its family must be identified as quickly as feasible. Experiments still have a long way to go before they can definitively differentiate between enzyme groups. There are six different kinds of enzymes that may be discovered in the human body. These include hydrolase, lyases, ligases, and isomerase. A brand-new method for identifying the enzyme family of proteins was developed by making use of a distance-based n-gram characteristic of pseudo amino acid composition. In order to do the analysis, we will employ a variety of equipment. Lerner's Support Vector Machine has several applications, some of which are K-Nearest Neighbor, Navier's Buyers, Random Forest, and Extra Tree (SVM). An estimation of the accuracy, precision, recall, and F1-score characteristics so they may be compared to other systems already in use.