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Predicting instances of pathway ontology classes for pathway integration | Journal of Biomedical Semantics | Full Text

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Background To improve the outcomes of biological pathway analysis, a better way of integrating pathway data is needed. Ontologies can be used to organize data from disparate sources, and we leverage the Pathway Ontology as a unifying ontology for organizing pathway data. We aim to associate pathway instances from different databases to the appropriate class in the Pathway Ontology. Results Using a supervised machine learning approach, we trained neural networks to predict mappings between Reactome pathways and Pathway Ontology (PW) classes. For 2222 Reactome classes, the neural network (NN) model generated 10,952 class recommendations. We compared against a baseline bag-of-words (BOW) model for predicting correct PW classes. A 5% subset of Reactome pathways (111 pathways) was randomly selected, and the corresponding class recommendations from both models were evaluated by two curators. The precision of the BOW model was higher (0.49 for BOW and 0.39 for NN), but the recall was lower (0.42 for BOW and 0.78 for NN). Around 78% of Reactome pathways received pertinent recommendations from the NN model. Conclusions The neural predictive model produced meaningful class recommendations that assisted PW curators in selecting appropriate class mappings for Reactome pathways. Our methods can be used to reduce the manual effort associated with ontology curation, and more broadly, for augmenting the curators’ ability to organize and integrate data from pathway databases using the Pathway Ontology.

Predicting instances of pathway ontology classes for pathway integration Research Open access Published: 13 June 2019 Volume 10 , article number 11 ( 2019 ) Cite this article You have full access to this open access article Download PDF Save article View saved research Journal of Biomedical Semantics Aims and scope Submit manuscript Predicting instances of pathway ontology classes for pathway integration Download PDF Abstract Background To improve the outcomes of biological pathway analysis, a better way of integrating pathway data is needed. Ontologies can be used to organize data from dispar

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