Abstract
The Linking Open Data (LOD) cloud is a global data space for publishing and linking structured data on the Web. The idea is to facilitate the integration, exchange, and processing of data. The LOD cloud already includes a lot of datasets that are related to the biological area. Nevertheless, most of the datasets about protein interactions do not use metadata standards. This means that they do not follow the LOD requirements and, consequently, hamper data integration. This problem has impacts on the information retrieval, specially with respect to datasets provenance and reuse in further prediction experiments. This paper proposes an ontology to describe and unite the four main kinds of data in a single prediction experiment environment: (i) information about the experiment itself; (ii) description and reference to the datasets used in an experiment; (iii) information about each protein involved in the candidate pairs. They correspond to the biological information that describes them and normally involves integration with other datasets; and, finally, (iv) information about the prediction scores organized by evidence and the final prediction. Additionally, we also present some case studies that illustrate the relevance of our proposal, by showing how queries can retrieve useful information.
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prefix ppi: <https://www.ypublish.info/protein_interaction_domain_ontology#>
prefix annot: <https://www.ypublish.info/protein_annotation_information#>
prefix prov: <https://www.ypublish.info/provenance_information#>
prefix result: <https://www.ypublish.info/prediction_results_information#>
prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
prefix biopax: <http://www.biopax.org/release/biopax-level2.owl#>.
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This work was partially funded by CAPES, CNPq, and FAPERJ.
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Martins, Y.C., Cavalcanti, M.C., Arge, L.W.P., Ziviani, A., de Vasconcelos, A.T.R. (2019). OntoPPI: Towards Data Formalization on the Prediction of Protein Interactions. In: Garoufallou, E., Fallucchi, F., William De Luca, E. (eds) Metadata and Semantic Research. MTSR 2019. Communications in Computer and Information Science, vol 1057. Springer, Cham. https://doi.org/10.1007/978-3-030-36599-8_23
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