Dataset Information

Predicting instances of pathway ontology classes for pathway integration.

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 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.

SUBMITTER: Wang LL

PROVIDER: S-EPMC6567466 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Predicting instances of pathway ontology classes for pathway integration.

Wang Lucy Lu LL Thomas Hayman G G Smith Jennifer R JR Tutaj Monika M Shimoyama Mary E ME Gennari John H JH

Journal of biomedical semantics 20190613 1

<h4>Background</h4>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.<h4>Results</h4>Using a supervised machine learning approach, we trained neural networks to predict mappings ...[more]

PMID: 31196182

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Dataset Information

Predicting instances of pathway ontology classes for pathway integration.

Publications

Predicting instances of pathway ontology classes for pathway integration.

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