Skip to main content

Table 5 Exemplifying results for Experiment II: Top twelve ranked candidate terms (highest cosine similarity) from the word embeddings created with CBOW and Skip-gram using two terms as target: “OLR1” from the abstract of the PubMed article with ID = 22,738,689; and “oxidized_low-density_lipoprotein receptor_ 1” that is the CVDO protein class name (rdfs:label) for the CVDO class gene with name (rdfs:label) OLR1. Hence, the target term exploits the protein class expressions within the CVDO

From: Deep learning meets ontologies: experiments to anchor the cardiovascular disease ontology in the biomedical literature

 

CBOW

Skip-gram

Rank

Candidate terms from word embeddings

Cosine

Candidate terms from word embeddings

Cosine

1

atherogenesis

0.469405

lectin-like_oxidized_low-density_lipoprotein

0.688603

2

atherosclerosis

0.465861

(LOX-1)_is

0.672042

3

CD36

0.439280

atherosclerosis_we_investigated

0.669050

4

LOX-1

0.424173

receptor-1

0.664891

5

atherosclerotic_lesion_formation

0.416537

lectin-like_oxidized_LDL_receptor-1

0.663988

6

vascular_inflammation

0.414620

lOX-1_is

0.660110

7

inflammatory_genes

0.411186

human_atherosclerotic_lesions

0.657075

8

atherosclerotic_lesions

0.405906

oxidized_low-density_lipoprotein_(ox-LDL)

0.655515

9

monocyte_chemoattractant_protein-1

0.398739

oxidized_low-density_lipoprotein_(oxLDL)

0.654965

10

plaque_destabilization

0.398201

(LOX-1)

0.652099

11

oxidized_low-density_lipoprotein_(oxLDL)

0.397967

proatherosclerotic

0.651571

12

atherosclerosis_atherosclerosis

0.396677

receptor-1_(LOX-1)_is

0.649000