OntoPPI: Towards Data Formalization on the Prediction of Protein Interactions

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.

SEEK ID: https://workflowhub.eu/publications/24

DOI: 10.1007/978-3-030-36599-8_23

Teams: yPublish - Bioinfo tools

Publication type: Journal

Journal: Metadata and Semantic Research

Book Title: Metadata and Semantic Research

Editors: Emmanouel Garoufallou and Francesca Fallucchi and Ernesto William De Luca

Publisher: Springer International Publishing

Citation: Metadata and Semantic Research 1057:260-271,Springer International Publishing

Date Published: 2019

Registered Mode: by DOI

Authors: Yasmmin Cortes Martins, Maria Cláudia Cavalcanti, Luis Willian Pacheco Arge, Artur Ziviani, Ana Tereza Ribeiro de Vasconcelos

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Citation
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 Metadata and Semantic Research (pp. 260–271). Springer International Publishing. https://doi.org/10.1007/978-3-030-36599-8_23
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Created: 23rd Oct 2023 at 15:09

Last updated: 23rd Oct 2023 at 15:12

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