OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction

Fatima Z. Smaili, Xin Gao, Robert Hoehndorf

Research output: Contribution to journalArticlepeer-review

79 Scopus citations


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. Annotation axioms 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. 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 metadata. We apply a Word2Vec model that has been pre-trained on either a corpus or abstracts or full-text articles 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 in a similarity measure to predict protein-protein interaction 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 using mouse model phenotypes. We demonstrate that OPA2Vec significantly outperforms existing methods for predicting gene-disease associations. Using evidence from mouse models, we apply OPA2Vec to identify candidate genes for several thousand rare and orphan diseases. 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.
Original languageEnglish (US)
Pages (from-to)2133-2140
Number of pages8
Issue number12
StatePublished - Nov 8 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): FCC/1/1976-04, FCC/1/1976-06, URF/1/2602-01, URF/1/3007-01, URF/1/3412-01, URF/1/3450-01, URF/1/3454-01
Acknowledgements: The research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. FCC/1/1976-04, FCC/1/1976-06, URF/1/2602-01, URF/1/3007-01, URF/1/3412-01, URF/1/3450-01 and URF/1/3454-01.


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