Graph-Theoretical Analysis of Biological Networks: A Survey

dc.authoridErciyes, Kayhan/0000-0001-9111-7156en_US
dc.contributor.authorErciyes, Kayhan
dc.date.accessioned2024-07-12T21:37:31Z
dc.date.available2024-07-12T21:37:31Z
dc.date.issued2023en_US
dc.department[Belirlenecek]en_US
dc.description.abstractBiological networks such as protein interaction networks, gene regulation networks, and metabolic pathways are examples of complex networks that are large graphs with small-world and scale-free properties. An analysis of these networks has a profound effect on our understanding the origins of life, health, and the disease states of organisms, and it allows for the diagnosis of diseases to aid in the search for remedial processes. In this review, we describe the main analysis methods of biological networks using graph theory, by first defining the main parameters, such as clustering coefficient, modularity, and centrality. We then survey fundamental graph clustering methods and algorithms, followed by the network motif search algorithms, with the aim of finding repeating subgraphs in a biological network graph. A frequently appearing subgraph usually conveys a basic function that is carried out by that small network, and discovering such a function provides an insight into the overall function of the organism. Lastly, we review network alignment algorithms that find similarities between two or more graphs representing biological networks. A conserved subgraph between the biological networks of organisms may mean a common ancestor, and finding such a relationship may help researchers to derive ancestral relationships and to predict the future evolution of organisms to enable the design of new drugs. We provide a review of the research studies in all of these methods, and conclude using the current challenging areas of biological network analysis, and by using graph theory and parallel processing for high performance analysis.en_US
dc.identifier.doi10.3390/computation11100188
dc.identifier.issn2079-3197
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-85175565249en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/computation11100188
dc.identifier.urihttps://hdl.handle.net/20.500.12415/6826
dc.identifier.volume11en_US
dc.identifier.wosWOS:001094179700001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofComputationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKY04168
dc.subjectBiological Networken_US
dc.subjectGraph Analysisen_US
dc.subjectClusteringen_US
dc.subjectNetwork Motifen_US
dc.subjectNetwork Alignmenten_US
dc.titleGraph-Theoretical Analysis of Biological Networks: A Surveyen_US
dc.typeArticle
dspace.entity.typePublication

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