A Survey of Network Embedding for Drug Analysis and Prediction.

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.
PMID: 32614745 [PubMed - as supplied by publisher]

Source: Current Protein and Peptide Science - July 1, 2020 Category: Biochemistry Authors: Liu Z, Chen Q, Lan W, Liang J, Chen YP, Chen B Tags: Curr Protein Pept Sci Source Type: research