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International Conference on Artificial Intelligence: Methodology, Systems, and Applications

AIMSA 2018: Artificial Intelligence: Methodology, Systems, and Applications pp 275–280Cite as

Semantic Annotation Modelling for Protein Functions Prediction

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11089))

Abstract

Functional protein annotation is a key phase in the analysis of de-novo sequenced genomes. Often the automatic annotation tools are insensitive to removing wrong annotations associated with contradictions and non-compliance in biological terms. In this study, we introduce a semantic model for representation of functional annotations based on a resource description framework standard (RDF).

We have integrated several databases with information for protein sequences and ontologies describing the functional relationships of the protein molecules. By using Web Ontology Language (OWL) axioms, RDF storage engines are able to take decisions which candidate annotations should be marked as biologically unviable and do not withstand the reality checks associated with coexistence, subcellular location and species affiliation [1]. This approach reduces the number of false positives and time spent in machine annotation’s curation process. The presented semantic data model is designed to combine the semantic representation of annotations with examples designed for machine learning.

Current work is part of a large scale project of functional annotation of plant genomes.

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Acknowledgements

The presented work has been funded by the Bulgarian NSF within the “GloBIG: A Model of Integration of Cloud Framework for Hybrid Massive Parallelism and its Application for Analysis and Automated Semantic Enhancement of Big Heterogeneous Data Collections” project, Contract DN02/9 of 17.12.2016, and by Sofia University SRF within the “Models for semantic integration of biomedical data” project, Contract 80-10-207/26.04.2018

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Authors and Affiliations

  1. AgroBioInstitute, 8 Dragan Tsankov Str., 1164, Sofia, Bulgaria

    Deyan Peychev

  2. Faculty of Mathematics and Informatics, Sofia University “St. Kliment Ohridski”, 5 James Bourchier Blvd, 1164, Sofia, Bulgaria

    Irena Avdjieva

Authors
  1. Deyan Peychev
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  2. Irena Avdjieva
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Correspondence to Irena Avdjieva .

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Editors and Affiliations

  1. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Gennady Agre

  2. Universität des Saarlandes, Saarbrücken, Germany

    Josef van Genabith

  3. DFKI GmbH, Saarbrücken, Germany

    Thierry Declerck

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Peychev, D., Avdjieva, I. (2018). Semantic Annotation Modelling for Protein Functions Prediction. In: Agre, G., van Genabith, J., Declerck, T. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2018. Lecture Notes in Computer Science(), vol 11089. Springer, Cham. https://doi.org/10.1007/978-3-319-99344-7_27

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  • DOI: https://doi.org/10.1007/978-3-319-99344-7_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99343-0

  • Online ISBN: 978-3-319-99344-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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