More Agility to Semantic Similarities Algorithm Implementations

Kostandinos Tsaramirsis, Georgios Tsaramirsis, Fazal Qudus Khan, Awais Ahmad, Alaa Omar Khadidos and Adil Khadidos
Additional contact information
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

IJERPH, 2019, vol. 17, issue 1, 1-12

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.

Keywords: Gene Ontology similarity algorithms; GO semantic terms similarity; gene/gene product semantic similarity; digital health (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/17/1/267/pdf (application/pdf)
https://www.mdpi.com/1660-4601/17/1/267/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:17:y:2019:i:1:p:267-:d:303444

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().