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More Agility to Semantic Similarities Algorithm Implementations

Author

Listed:
  • Kostandinos Tsaramirsis

    (Infosuccess3D, 55 Navarxou Kountourgiotou Road, Aigaleo, 122 42 Athens, Greece)

  • Georgios Tsaramirsis

    (Department of Information Technology, Faculty of Computing And IT, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Fazal Qudus Khan

    (Department of Information Technology, Faculty of Computing And IT, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Awais Ahmad

    (Dipartimento di informatica, universita’ degli Studi di Milano, 20122 Milan, Italy)

  • Alaa Omar Khadidos

    (Department of Information Systems, Faculty of Computing And IT, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Adil Khadidos

    (Department of Information Technology, Faculty of Computing And IT, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

Abstract

Algorithms for measuring semantic similarity between Gene Ontology (GO) terms has become a popular area of research in bioinformatics as it can help to detect functional associations between genes and potential impact to the health and well-being of humans, animals, and plants. While the focus of the research is on the design and improvement of GO semantic similarity algorithms, there is still a need for implementation of such algorithms before they can be used to solve actual biological problems. This can be challenging given that the potential users usually come from a biology background and they are not programmers. A number of implementations exist for some well-established algorithms but these implementations are not generic enough to support any algorithm other than the ones they are designed for. The aim of this paper is to shift the focus away from implementation, allowing researchers to focus on algorithm’s design and execution rather than implementation. This is achieved by an implementation approach capable of understanding and executing user defined GO semantic similarity algorithms. Questions and answers were used for the definition of the user defined algorithm. Additionally, this approach understands any direct acyclic digraph in an Open Biomedical Ontologies (OBO)-like format and its annotations. On the other hand, software developers of similar applications can also benefit by using this as a template for their applications.

Suggested Citation

  • Kostandinos Tsaramirsis & Georgios Tsaramirsis & Fazal Qudus Khan & Awais Ahmad & Alaa Omar Khadidos & Adil Khadidos, 2019. "More Agility to Semantic Similarities Algorithm Implementations," IJERPH, MDPI, vol. 17(1), pages 1-12, December.
    • Handle: RePEc:gam:jijerp:v:17:y:2019:i:1:p:267-:d:303444
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    References listed on IDEAS

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    1. Catia Pesquita & Daniel Faria & André O Falcão & Phillip Lord & Francisco M Couto, 2009. "Semantic Similarity in Biomedical Ontologies," PLOS Computational Biology, Public Library of Science, vol. 5(7), pages 1-12, July.
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