Drug-Disease Association Prediction Based on Neighborhood Information Aggregation in Neural Networks

Abstract

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.

Keywords

• Drug reposition
• deep learning
• matrix decomposition
• heterogeneous network
• end to end