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Traditional network-based computational methods have shown good results in drug analysis and prediction.
However, these methods are time consuming and lack universality, and it is difficult to exploit the auxiliary information of
nodes and edges. Network embedding provides a promising way for alleviating the above problems by transforming network into a low-dimensional space while preserving network structure and auxiliary information. This thus facilitates the
application of machine learning algorithms for subsequent processing. Network embedding has been introduced into drug
analysis and prediction in the last few years, and has shown superior performance over traditional methods. However, there
is no systematic review of this issue. This article offers a comprehensive survey of the primary network embedding methods
and their applications in drug analysis and prediction. The network embedding technologies applied in homogeneous network and heterogeneous network are investigated and compared, including matrix decomposition, random walk, and deep
learning. Especially, the Graph neural network (GNN) methods in deep learning are highlighted. Further, the applications of
network embedding in drug similarity estimation, drug-target interaction prediction, adverse drug reactions prediction, protein function and therapeutic peptides prediction are discussed. Several future potential research directions are also discussed.
Design-based random permutation models with auxiliary information
