HBP-SRF: an intelligent model for prediction of Hormone Binding Proteins using Statistical Moment

Abstract

The Hormone binding proteins (HBPs) is a protein type that reaches itself to the targeted hormones to regulate it. Inside the cell it resides in the outer region of growth hormone receptor and helps in the growth of cells inside the organisms. Due to its several advantages, it is important to identify the molecular mechanism of HBPs. Thus, researcher has developed computational model for the prediction of these proteins and achieved very good result, but further improvement is required to achieve more accurate results. The purpose of this study is to develop a computational model that can predict HBPs with high accuracy than the existing model. This model is constructed using statistical moment feature extraction method along with Random Forest Classification algorithm and assessed using Jackknife testing and 10-fold cross validation. The results obtained by this model are 94.5% for jackknife testing and 95.14% for 10-fold cross validation. The model performed well and obtained remarkable results as compared the previous models available in literature.