Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.11.07.372466v1?rss=1 Authors: Mahbub, S., Bayzid, M. S. Abstract: Motivation: Protein-protein interactions are central to most biological processes. However, reliable identification of protein-protein interaction (PPI) sites using conventional experimental methods is slow and expensive. Therefore, great efforts are being put into computational methods to identify PPI sites. Results: We present EGAT, a highly accurate deep learning based method for PPI site prediction, where we have introduced a novel edge aggregated graph attention network to effectively leverage the structural information. We, for the first time, have used transfer learning in PPI site prediction. Our proposed edge aggregated network, together with transfer learning, has achieved remarkable improvement over the best alternate methods. Furthermore, EGAT offers a more interpretable framework than the typical black-box deep neural networks. Availability: EGAT is freely available as an open source project at https://github.com/Sazan-Mahbub/EGAT. Copy rights belong to original authors. Visit the link for more info