Journal
JOURNAL OF BIOMEDICAL SEMANTICS
Volume 8, Issue -, Pages -Publisher
BMC
DOI: 10.1186/s13326-017-0144-y
Keywords
Biological ontology; Semantic similarity; Human phenotype ontology
Categories
Funding
- National Natural Science Foundation of China [61,571,152, 61,502,125]
- National High- tech RAMP
- D Program of China (863 Program) [2014AA021505, 2015AA020101, 2015AA020108]
- National Science and Technology Major Project [2013ZX03005012, 2016YFC1202302]
- Heilongjiang Postdoctoral Fund [LBH- Z15179]
- China Postdoctoral Science Foundation [2016 M590291]
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Background: Although disease diagnosis has greatly benefited from next generation sequencing technologies, it is still difficult to make the right diagnosis purely based on sequencing technologies for many diseases with complex phenotypes and high genetic heterogeneity. Recently, calculating Human Phenotype Ontology (HPO)-based phenotype semantic similarity has contributed a lot for completing disease diagnosis. However, factors which affect the accuracy of HPO-based semantic similarity have not been evaluated systematically. Results: In this study, we proposed a new framework called HPOFactor to evaluate these factors. Our model includes four components: (1) the size of annotation set, (2) the evidence code of annotations, (3) the quality of annotations and (4) the coverage of annotations respectively. Conclusions: HPOFactor analyzes the four factors systematically based on two kinds of experiments: causative gene prediction and disease prediction. Furthermore, semantic similarity measurement could be designed based on the characteristic of these factors.
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