Unearthing Insights into Metabolic Syndrome by Linking Drugs, Targets, and Gene Expressions Using Similarity Measures and Graph Theory

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dc.authorid0000-0001-6247-8673en_US
dc.contributor.authorZafar, Alwaz
dc.contributor.authorWajid, Bilal
dc.contributor.authorShabbir, Ans
dc.contributor.authorAwan, Fahim Gohar
dc.contributor.authorAhsan, Momina
dc.contributor.authorAhmad, Sarfraz
dc.contributor.authorWajid, Imran
dc.contributor.authorAnwar, Faria
dc.contributor.authorMazhar, Fazeelat
dc.date.accessioned2024-06-24T07:17:25Z
dc.date.available2024-06-24T07:17:25Z
dc.date.issued2024en_US
dc.departmentEnstitüler, Sosyal Bilimler Enstitüsü, İşletme Ana Bilim Dalıen_US
dc.description.abstractAims and Objectives: Metabolic syndrome (MetS) is a group of metabolic disorders that includes obesity in combination with at least any two of the following conditions, i.e., insulin resistance, high blood pressure, low HDL cholesterol, and high triglycerides level. Treatment of this syndrome is challenging because of the multiple interlinked factors that lead to increased risks of type-2 diabetes and cardiovascular diseases. This study aims to conduct extensive insilico analysis to (i) find central genes that play a pivotal role in MetS and (ii) propose suitable drugs for therapy. Our objective is to first create a drug-disease network and then identify novel genes in the drug-disease network with strong associations to drug targets, which can help in increasing the therapeutical effects of different drugs. In the future, these novel genes can be used to calculate drug synergy and propose new drugs for the effective treatment of MetS. Methods: For this purpose, we (i) investigated associated drugs and pathways for MetS, (ii) employed eight different similarity measures to construct eight gene regulatory networks, (iii) chose an optimal network, where a maximum number of drug targets were central, (iv) determined central genes exhibiting strong associations with these drug targets and associated disease-causing pathways, and lastly (v) employed these candidate genes to propose suitable drugs. Results: Our results indicated (i) a novel drug-disease network complex, with (ii) novel genes associated with MetS. Conclusion: Our developed drug-disease network complex closely represents MetS with associated novel findings and markers for an improved understanding of the disease and suggested therapy.en_US
dc.identifier.doi10.2174/1573409920666230817101913en_US
dc.identifier.endpage783en_US
dc.identifier.issue6en_US
dc.identifier.pmid37592790en_US
dc.identifier.scopus2-s2.0-85195268417en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage773en_US
dc.identifier.urihttps://hdl.handle.net/11467/7305
dc.identifier.urihttps://doi.org/10.2174/1573409920666230817101913
dc.identifier.volume20en_US
dc.identifier.wosWOS:001247953700005en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherBentham Science Publishersen_US
dc.relation.ispartofCurrent Computer-Aided Drug Designen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/restrictedAccessen_US
dc.subjectMetabolic syndrome, Diabetes, Cardiovascular disease, Drugs, Graph theory, Gene regulatory networksen_US
dc.titleUnearthing Insights into Metabolic Syndrome by Linking Drugs, Targets, and Gene Expressions Using Similarity Measures and Graph Theoryen_US
dc.typeArticleen_US

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