Deep Neural Network and Extreme Gradient Boosting Based Hybrid Classifier for Improved Prediction of Protein-Protein Interaction

• Published in: IEEE/ACM transactions on computational biology and bioinformatics 2022-01, Vol.19 (1), p.155-165
• Main Authors: Mahapatra, Satyajit, Gupta, Vivek Raj, Sahu, Sitanshu Sekhar, Panda, Ganapati
• Format: Article
• Language: English
• Subjects: Amino acid composition; Amino acid sequence; Amino Acids; Artificial neural networks; Boosting; Classifiers; Composition; Datasets; deep neural network; Encoding; extreme gradient boosting; Feature extraction; Fungi; Humans; hybrid classifier; information fusion; Neural networks; Neural Networks, Computer; Pathogenesis; Predictions; Protein interaction; Protein Interaction Mapping; Protein sequence; Protein-protein interaction; Proteins; Saccharomyces cerevisiae; Saccharomyces cerevisiae - genetics; Signal transduction; Social behavior; Software; Support vector machines; Therapeutic targets; Yeast
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2021.3061300

Understanding the behavioral process of life and disease-causing mechanism, knowledge regarding protein-protein interactions (PPI) is essential. In this paper, a novel hybrid approach combining deep neural network (DNN) and extreme gradient boosting classifier (XGB) is employed for predicting PPI. The hybrid classifier (DNN-XGB) uses a fusion of three sequence-based features, amino acid composition (AAC), conjoint triad composition (CT), and local descriptor (LD) as inputs. The DNN extracts the hidden information through a layer-wise abstraction from the raw features that are passed through the XGB classifier. The 5-fold cross-validation accuracy for intraspecies interactions dataset of Saccharomyces cerevisiae (core subset), Helicobacter pylori, Saccharomyces cerevisiae, and Human are 98.35, 96.19, 97.37, and 99.74 percent respectively. Similarly, accuracies of 98.50 and 97.25 percent are achieved for interspecies interaction dataset of Human- Bacillus Anthracis and Human- Yersinia pestis datasets, respectively. The improved prediction accuracies obtained on the independent test sets and network datasets indicate that the DNN-XGB can be used to predict cross-species interactions. It can also provide new insights into signaling pathway analysis, predicting drug targets, and understanding disease pathogenesis. Improved performance of the proposed method suggests that the hybrid classifier can be used as a useful tool for PPI prediction. The datasets and source codes are available at: https://github.com/SatyajitECE/DNN-XGB-for-PPI-Prediction .