<reponame>coolmaksat/academic-kickstart<gh_stars>0
@article{evaluating2017,
 abstract = {Background
Ontologies are widely used as metadata in biological and biomedical
datasets. Measures of semantic similarity utilize
ontologies to determine how similar two entities
annotated with classes from ontologies are, and
semantic similarity is increasingly applied in
applications ranging from diagnosis of disease to
investigation in gene networks and functions of gene
products.

Results
Here, we analyze a large number of semantic similarity measures and
the sensitivity of similarity values to the number
of annotations of entities, difference in annotation
size and to the depth or specificity of annotation
classes. We find that most similarity measures are
sensitive to the number of annotations of entities,
difference in annotation size as well as to the
depth of annotation classes; well-studied and richly
annotated entities will usually show higher
similarity than entities with only few annotations
even in the absence of any biological relation.

Conclusions
Our findings may have significant impact on the interpretation of
results that rely on measures of semantic
similarity, and we demonstrate how the sensitivity
to annotation size can lead to a bias when using
semantic similarity to predict protein-protein
interactions.},
 author = {<NAME> and Hoehndorf, Robert},
 doi = {10.1186/s13326-017-0119-z},
 issn = {2041-1480},
 journal = {Journal of Biomedical Semantics},
 month = {feb},
 number = {1},
 pages = {7},
 title = {Evaluating the effect of annotation size on measures of semantic similarity},
 url = {https://doi.org/10.1186/s13326-017-0119-z},
 urldate = {2018-11-05},
 volume = {8},
 year = {2017}
}

