Predicting instances of pathway ontology classes for pathway integration. J Biomed Semantics 2019 Jun 13;10(1):11
Date
06/15/2019Pubmed ID
31196182Pubmed Central ID
PMC6567466DOI
10.1186/s13326-019-0202-8Scopus ID
2-s2.0-85067289748 (requires institutional sign-in at Scopus site) 2 CitationsAbstract
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.
Author List
Wang LL, Thomas Hayman G, Smith JR, Tutaj M, Shimoyama ME, Gennari JHAuthor
Monika Tutaj Research Scientist II in the Physiology department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
Biological OntologiesSupervised Machine Learning
