The computational methods of protein-protein interaction sites prediction can effectively avoid the shortcomings of high cost and time in traditional experimental approaches. However, the serious class imbalance between interface and non-interface residues on the protein sequences limits the prediction performance of these methods. This work therefore proposed a new strategy, NearMiss-based under-sampling for unbalancing datasets and Random Forest classification (NM-RF), to predict protein interaction sites. Herein, the residues on protein sequences were represented by the PSSM-derived features, hydropathy index (HI) and relative solvent accessibility (RSA). In order to resolve the class imbalance problem, an under-sampling method based on NearMiss algorithm is adopted to remove some non-interface residues, and then the random forest algorithm is used to perform binary classification on the balanced feature datasets. Experiments show that the accuracy of NM-RF model reaches 87.6% and 84.3% on Dtestset72 and PDBtestset164 respectively, which demonstrate the effectiveness of the proposed NM-RF method in differentiating the interface or non-interface residues.

Drug effectiveness describes the capacity of a drug to cure a disease, which is of great importance for drug safety. To get this information, a number of real-world patient-oriented outcomes are required. However, current surveillance systems can only capture a small portion of them, and there is a time lag in processing the reported data. Since social media provides quantities of patient-oriented user posts in real-time, it is of great value to automatically extract drug effectiveness from these data. To this end, we build a dataset containing 25K tweets describing drug use, and further harvest drug effectiveness by performing Relation Extraction (RE) between chemicals and diseases. Most prior works about RE deal with mention pairs independently, which is not suitable for our task since interactions across mention pairs are widespread. In this paper, we propose a model regarding mention pairs as nodes connected by multiple types of edges. With the help of graph-based information transfers over time, it deals with all mention pairs simultaneously to capture their interactions. Besides, a novel idea is used to perform multiple instance learning, a big challenge in general RE tasks. Extensive experimental results show that our model outperforms previous work by a substantial margin.