Identifying protein subcellular locations is an important topic in protein function prediction. Interacting proteins may share similar locations. Thus, it is imperative to infer protein subcellular locations by taking protein-protein interactions (PPIs)into account. In this study, we present a network embedding-based method, node2loc, to identify protein subcellular locations. node2loc first learns distributed embeddings of proteins in a protein-protein interaction (PPI)network using node2vec. Then the learned embeddings are further fed into a recurrent neural network (RNN). To resolve the severe class imbalance of different subcellular locations, Synthetic Minority Over-sampling Technique (SMOTE)is applied to artificially synthesize proteins for minority classes. node2loc is evaluated on our constructed human benchmark dataset with 16 subcellular locations and yields a Matthews correlation coefficient (MCC)value of 0.800, which is superior to baseline methods. In addition, node2loc yields a better performance on a Yeast benchmark dataset with 17 locations. The results demonstrate that the learned representations from a PPI network have certain discriminative ability for classifying protein subcellular locations. However, node2loc is a transductive method, it only works for proteins connected in a PPI network, and it needs to be retrained for new proteins. In addition, the PPI network needs be annotated to some extent with location information. node2loc is freely available at https://github.com/xypan1232/node2loc .

Under debt obligation promises, small- and medium-sized enterprises (SMEs) can guarantee each other to enhance their financial security to get loans from commercial banks. When the economy rises, the banks may reduce the threshold to some extent, which may introduce default risk during the economy down period, especially when many SMEs bind together and form complex networks. The risk may diffuse across the guarantee network and may result in a financial crisis. Macroprudential oversight of the guarantee network to eliminate any potential systematics financial risk is the central task of the regulatory commission and the commercial banks. Based on our observation, the delinquent probability of an SME depends not only on self-financial status but also highly related to its temporal behaviors and structural position in networks. The classic approach for loan assessment criteria face challenges in extracting temporal and structural patterns from dynamic networks. To address these issues, we propose a temporal delinquent event prediction (TDEP) framework that preserves temporal network structures and credit behavior sequences in an end-to-end model. In particular, we first employ a graph attention layer to learn the representation of nodes in temporal guarantee networks. We then design a recursive and self-attention mechanism to integrate both credit behavior and network structure information. The learned attentional weights are leveraged to uncover high-risk guarantee patterns that effectively accelerate the risk assessment process. Afterward, we conduct extensive experiments in a real-world guaranteed-loan data set to evaluate its performance. The results show the effectiveness of our proposed approach compared with the state-of-the-art baselines. Finally, we integrate the proposed model in a real-world loan risk management system. We present the implementation details of each subcomponent of the system and report out the performance after online deployment.