  Motivation: Ontologies are widely used in biology for data annotation,
integration, and analysis. In addition to formally structured axioms,
ontologies contain meta-data in the form of annotation axioms which provide
valuable pieces of information that characterize ontology classes. Annotations
commonly used in ontologies include class labels, descriptions, or synonyms.
Despite being a rich source of semantic information, the ontology meta-data are
generally unexploited by ontology-based analysis methods such as semantic
similarity measures. Results: We propose a novel method, OPA2Vec, to generate
vector representations of biological entities in ontologies by combining formal
ontology axioms and annotation axioms from the ontology meta-data. We apply a
Word2Vec model that has been pre-trained on PubMed abstracts to produce feature
vectors from our collected data. We validate our method in two different ways:
first, we use the obtained vector representations of proteins as a similarity
measure to predict protein-protein interaction (PPI) on two different datasets.
Second, we evaluate our method on predicting gene-disease associations based on
phenotype similarity by generating vector representations of genes and diseases
using a phenotype ontology, and applying the obtained vectors to predict
gene-disease associations. These two experiments are just an illustration of
the possible applications of our method. OPA2Vec can be used to produce vector
representations of any biomedical entity given any type of biomedical ontology.
Availability: https://github.com/bio-ontology-research-group/opa2vec Contact:
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