<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>36</volume><submitter>Hridoy HM</submitter><pubmed_abstract>Atherosclerosis (ATH) is a chronic cardiovascular disease characterized by plaque formation in arteries, and it is a major cause of illness and death. Although therapeutic advances have significantly improved the prognosis of ATH, missing therapeutic targets pose a significant residual threat. This research used a systems biology approach to identify the molecular biomarkers involved in the onset and progression of ATH, analysing microarray gene expression datasets from ATH and tissues impacted by risk factors such as high cholesterol, adipose tissue, smoking, obesity, sedentary lifestyle, stress, alcohol consumption, hypertension, hyperlipidaemia, high fat, diabetes to find the differentially expressed genes (DEGs). Bioinformatic analyses of Protein-Protein Interaction (PPI), Gene Ontolog</pubmed_abstract><journal>Biochemistry and biophysics reports</journal><pagination>101574</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC10652116</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>In silico based analysis to explore genetic linkage between atherosclerosis and its potential risk factors.</pubmed_title><pmcid>PMC10652116</pmcid><pubmed_authors>Haidar MN</pubmed_authors><pubmed_authors>Khatun C</pubmed_authors><pubmed_authors>Hossain MP</pubmed_authors><pubmed_authors>Aziz MA</pubmed_authors><pubmed_authors>Sarker A</pubmed_authors><pubmed_authors>Hridoy HM</pubmed_authors><pubmed_authors>Hossain MT</pubmed_authors></additional><is_claimable>false</is_claimable><name>In silico based analysis to explore genetic linkage between atherosclerosis and its potential risk factors.</name><description>Atherosclerosis (ATH) is a chronic cardiovascular disease characterized by plaque formation in arteries, and it is a major cause of illness and death. Although therapeutic advances have significantly improved the prognosis of ATH, missing therapeutic targets pose a significant residual threat. This research used a systems biology approach to identify the molecular biomarkers involved in the onset and progression of ATH, analysing microarray gene expression datasets from ATH and tissues impacted by risk factors such as high cholesterol, adipose tissue, smoking, obesity, sedentary lifestyle, stress, alcohol consumption, hypertension, hyperlipidaemia, high fat, diabetes to find the differentially expressed genes (DEGs). Bioinformatic analyses of Protein-Protein Interaction (PPI), Gene Ontolog</description><dates><release>2023-01-01T00:00:00Z</release><publication>2023 Dec</publication><modification>2025-04-26T21:15:08.213Z</modification><creation>2025-02-19T00:38:23.576Z</creation></dates><accession>S-EPMC10652116</accession><cross_references><pubmed>38024867</pubmed><doi>10.1016/j.bbrep.2023.101574</doi></cross_references></HashMap>