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Article

More Agility to Semantic Similarities Algorithm Implementations

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Infosuccess3D, 55 Navarxou Kountourgiotou Road, Aigaleo, 122 42 Athens, Greece
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Department of Information Technology, Faculty of Computing And IT, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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Dipartimento di informatica, universita’ degli Studi di Milano, 20122 Milan, Italy
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Department of Information Systems, Faculty of Computing And IT, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(1), 267; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17010267
Received: 8 December 2019 / Revised: 22 December 2019 / Accepted: 28 December 2019 / Published: 30 December 2019
(This article belongs to the Special Issue Computing Techniques for Environmental Research and Public Health)
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. View Full-Text
Keywords: Gene Ontology similarity algorithms; GO semantic terms similarity; gene/gene product semantic similarity; digital health Gene Ontology similarity algorithms; GO semantic terms similarity; gene/gene product semantic similarity; digital health
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MDPI and ACS Style

Tsaramirsis, K.; Tsaramirsis, G.; Khan, F.Q.; Ahmad, A.; Khadidos, A.O.; Khadidos, A. More Agility to Semantic Similarities Algorithm Implementations. Int. J. Environ. Res. Public Health 2020, 17, 267. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17010267

AMA Style

Tsaramirsis K, Tsaramirsis G, Khan FQ, Ahmad A, Khadidos AO, Khadidos A. More Agility to Semantic Similarities Algorithm Implementations. International Journal of Environmental Research and Public Health. 2020; 17(1):267. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17010267

Chicago/Turabian Style

Tsaramirsis, Kostandinos, Georgios Tsaramirsis, Fazal Q. Khan, Awais Ahmad, Alaa O. Khadidos, and Adil Khadidos. 2020. "More Agility to Semantic Similarities Algorithm Implementations" International Journal of Environmental Research and Public Health 17, no. 1: 267. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17010267

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