Predicting biomedical relationships using the knowledge and graph embedding cascade model.

ABSTRACT: Advances in machine learning and deep learning methods, together with the increasing availability of large-scale pharmacological, genomic, and chemical datasets, have created opportunities for identifying potentially useful relationships within biochemical networks. Knowledge embedding models have been found to have value in detecting knowledge-based correlations among entities, but little effort has been made to apply them to networks of biochemical entities. This is because such networks tend to be unbalanced and sparse, and knowledge embedding models do not work well on them. However, to some extent, the shortcomings of knowledge embedding models can be compensated for if they are used in association with graph embedding. In this paper, we combine knowledge embedding and graph embedding

SUBMITTER: Liang X 

PROVIDER: S-EPMC6565371 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature