To date, there are no effective treatments for most neurodegenerative
diseases. Knowledge graphs can provide comprehensive and semantic
representation for heterogeneous data, and have been successfully leveraged in
many biomedical applications including drug repurposing. Our objective is to
construct a knowledge graph from literature to study relations between
Alzheimer's disease (AD) and chemicals, drugs and dietary supplements in order
to identify opportunities to prevent or delay neurodegenerative progression. We
collected biomedical annotations and extracted their relations using SemRep via
SemMedDB. We used both a BERT-based classifier and rule-based methods during
data preprocessing to exclude noise while preserving most AD-related semantic
triples. The 1,672,110 filtered triples were used to train with knowledge graph
completion algorithms (i.e., TransE, DistMult, and ComplEx) to predict
candidates that might be helpful for AD treatment or prevention. Among three
knowledge graph completion models, TransE outperformed the other two (MR =
13.45, Hits@1 = 0.306). We leveraged the time-slicing technique to further
evaluate the prediction results. We found supporting evidence for most highly
ranked candidates predicted by our model which indicates that our approach can
inform reliable new knowledge. This paper shows that our graph mining model can
predict reliable new relationships between AD and other entities (i.e., dietary
supplements, chemicals, and drugs). The knowledge graph constructed can
facilitate data-driven knowledge discoveries and the generation of novel
hypotheses.Comment: Submitted to the BMC Bioinformatic