Highly-Confident Protein Interactome Prediction via Variational Autoencoder

Protein-protein interactions (PPIs) play a critical role in cellular activities. However, discover them experimentally is exhausted. How to predict the missing PPIs with the known PPI networks (PPINs) is a challenging task considering their large scale and extreme sparsity. To utilize the known data efficiently, this work proposes a Highly-Confident Protein Interactome Prediction (HPIP) model with two-fold ideas: a) employing a variational autoencoder model based on Gaussian distribution as a basic PPI predictor for processing the sparse input PPIN; b) embedding the basic PPI predictor into an elastic architecture which based on a block and probability summing strategy. Experimental results on two real world PPINs from the STRING database indicate that HPIP can effectively predict PPIs with high confidence.