Computational drug repositioning plays a vital role in the prediction of drug function. Many new functions discovered have been confirmed. In comparison with traditional drug repositioning, computational drug repositioning shortens the time and reduces labor. Thus, it has received wide attention in recent years. However, prediction remains a considerable challenge. In this paper, a method called HNRD is introduced to predict the link between drugs and diseases. It is based on neighborhood information aggregation in neural networks which combines the similarity of diseases and drugs, the associations between the drugs and diseases. Compared with the state-of-the-art method before, our method has achieved better results, with the best AUC of 0.97 in one of the golden datasets. To better evaluate our approach, we also performed data analysis based on one-to-one association’s prediction and robust analysis by testing on different datasets. All the results prove the excellent performance of prediction. Source codes of this paper are available on https://github.com/heibaipei/HNRD .

In the past few decades, identification recognition based on electroencephalography (EEG) has received extensive attention to resolve the security problems of conventional biometric systems. In the present study, a novel EEG-based identification system with different entropy and a continuous convolution neural network (CNN) classifier is proposed. The performance of the proposed method is experimentally evaluated through the emotional EEG data. The conducted experiment shows that the proposed method approaches the stunning accuracy (ACC) of 99.7% on average and can rapidly train and update the DE-CNN model. Then, the effects of different emotions and the impact of different time intervals on the identification performance are investigated. Obtained results show that different emotions affect the identification accuracy, where the negative and neutral mood EEG has a better robustness than positive emotions. For a video signal as the EEG stimulant, it is found that the proposed method with 0–75 Hz is more robust than a single band, while the 15–32 Hz band presents overfitting and reduces the accuracy of the cross-emotion test. It is concluded that time interval reduces the accuracy and the 15–32 Hz band has the best compatibility in terms of the attenuation.