Prediction of Drug–Target Interactions From Multi-Molecular Network Based on Deep Walk Embedding Model - Chen, Zhan-Heng; You, Zhu-Hong; Guo, Zhen-Hao; Yi, Hai-Cheng; Luo, Gong-Xu; Wang, Yan-Bin (Frontiers in Bioengineering and Biotechnology, 2020)
Predicting drug-target interactions (DTIs) is crucial in the innovative drug discovery, drug repositioning and other fields. However, there are many shortcomings for predicting drug-target interactions using traditional biological experimental methods, such as high-cost, time-consuming, low efficiency and so on, which make it difficult to be widely applied. As a supplement, the in silico method can provide helpful information for DTIs prediction in a timely behavior. In this work, a deep walk embedding method is developed for predicting DTIs from multi-molecular network. More specifically, a multi-molecular network, also called molecular associations network (MAN), is constructed by integrating the associations among drug, protein, disease, lncRNA and miRNA. Then, each node can be represented as a behavior feature vector by using deep walk embedding method. Finally, we compared behavior feature with traditional attribute feature on integrated dataset by using various classifiers. The experimental results revealed that the behavior feature could be performed better on different classifiers, especially on the random forest classifier. It is also demonstrated that the use of behavior information is very helpful for addressing the problem of sequences containing both self-interacting and non-interacting pairs of proteins. This work is not only extremely suitable for predicting DTIs, but also provides a new perspective for other biomolecules associations prediction.