Prediction of protein-protein interactions based on elastic net and deep forest

Abstract

Prediction of protein-protein interactions (PPIs) helps to grasp molecular roots of 14 disease. However, web-lab experiments to predict PPIs are limited and costly. Using 15 machine-learning-based frameworks can not only automatically identify PPIs, but also 16 provide new ideas for drug research and development from a promising alternative. We 17 present a novel deep-forest-based method for PPIs prediction. First, pseudo amino acid 18 composition (PAAC), autocorrelation descriptor (Auto), multivariate mutual information 19 (MMI), composition-transition-distribution (CTD), and amino acid composition PSSM 20 (AAC-PSSM), and dipeptide composition PSSM (DPC-PSSM) are adopted to extract and 21 construct the pattern of PPIs. Secondly, elastic net is utilized to optimize the initial feature 22 vectors and boost the predictive performance. Finally, GcForest-PPI model based on deep 23 forest is built up. Benchmark experiments reveal that the accuracy values of Saccharomyces 24 cerevisiae and Helicobacter pylori are 95.44% and 89.26%. We also apply GcForest-PPI on 25 independent test sets and CD9-core network, crossover network, and cancer-specific network. 26The evaluation shows that GcForest-PPI can boost the prediction accuracy, complement 27 experiments and improve drug discovery. The datasets and code of GcForest-PPI could be 28 downloaded at https://github.com/QUST-AIBBDRC/GcForest-PPI/. 29