Deep Transfer Learning Based PPI Prediction for Protein Complex Detection

Abstract

This paper deals with the problem of detecting protein complexes from protein-protein interaction (PPI) network using a spectral clustering method. A complete PPI network is crucial for detection performance. However, experimentally identified PPIs are usually very limited, resulting in incomplete PPI networks. To solve this problem, we propose a deep transfer learning based predictor for the PPI prediction, consisting of a semi-supervised SVM classifier and a deep feature extractor of convolution neural network (CNN). Considering the fact that the similarities of gene ontology (GO) annotations contribute to protein interaction, and the difference of subcellular localizations contribute to negative interactions, we pre-train the deep CNN feature extractor in deep GO annotation and subcellular localization predictors and then transfer it to the PPI prediction. In this way, we have a deep PPI detector enhanced with transfer learning of GO annotation and subcellular localization prediction. Experimental results show that the proposed method outperforms the state-of-the-art methods on benchmark datasets.