Deep-GHBP: Improving prediction of Growth Hormone-binding proteins using deep learning model. (September 2022)

• Record Type: Journal Article
• Authors: Ali, Farman; Kumar, Harish; Patil, Shruti; Ahmad, Ashfaq; Babour, Amal; Daud, Ali
• Is Part Of: Biomedical signal processing and control. Volume 78(2022)
• Publication Date: 2022-09
• Subjects: Deep Neural Network (DNN) -- Growth Hormone-binding Proteins (GHBPs) -- Position Specific Scoring Matrix (PSSM) -- Random Forest (RF) -- Support Vector Machine (SVM); DNN Deep Neural Network -- SVM Support Vector Machine -- RF Random Forest -- GHBPs Growth Hormone Binding Proteins -- PSSM Position Specific Scoring Matrix; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103856
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Designed a novel predictor named Deep-GHBP for prediction of Growth Hormone-binding Proteins. The local and global discriminative features are explored by Position Specific Scoring Matrix (PSSM), Composition Transition Distribution (CTD), Geary, Pseudo Amino Acid Composition (PseAAC), and Moran features. The best features are selected by a novel Multi-Model Consensus (MMC) feature selection algorithm. Deep Neural Network (DNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) used as classification algorithms. Deep-GHBP has secured the highest prediction results for GHBP. Abstract: Growth hormone-binding proteins (GHBPs) are carrier proteins that interact with other growth hormone proteins in a selective and non-covalent fashion. GHBPs perform significant roles in various biological activities including cell growth, granular cellular mechanism, and therapeutic approaches. Considering these crucial functions, an accurate prediction of GHBPs is indispensable. In this connection, wet-laboratory and machine learning (ML) models have been developed. However, these methods have a limited amount of performance, including less informative features or inefficient learning models. This study presents an innovative approach by employing a Position Specific Scoring Matrix (PSSM), Composition Transition Distribution (CTD), Geary, Moran, and Pseudo Amino Acid Composition (PseAAC). The important features of these were selected by a novel Multi-Model Consensus (MMC) feature selection algorithm. The most appropriate feature set was then provided to four classifiers: Deep Neural Network (DNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The DNN on the optimal feature set achieved the highest accuracies of 88.09% and 83.33% on the training and testing datasets, respectively. The achieved results were also superior to existing predictors for the identification of GHBPs. Thus, GHBP-Pred will be significantly helpful for large scale prediction of GHBPs with high precision.