Relation path feature embedding based convolutional neural network method for drug discovery.

ABSTRACT:

Background

Drug development is an expensive and time-consuming process. Literature-based discovery has played a critical role in drug development and may be a supplementary method to help scientists speed up the discovery of drugs.

Methods

Here, we propose a relation path features embedding based convolutional neural network model with attention mechanism for drug discovery from literature, which we denote as PACNN. First, we use predications from biomedical abstracts to construct a biomedical knowledge graph, and then apply a path ranking algorithm to extract drug-disease relation path features on the biomedical knowledge graph. After that, we use these drug-disease relation features to train a convolutional neural network model which combined with the attention mechanism.

SUBMITTER: Zhao D 

PROVIDER: S-EPMC6454669 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature