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We used literature-based discovery to identify and prioritize multi-scalar explanatory biology that relates resistant hypertension to COVID-19. Cross-domain text mining of 33+ million PubMed articles within a comprehensive knowledge graph was performed using SemNet 2.0. Unsupervised rank aggregation determined which concepts were most relevant utilizing the normalized HeteSim score. A series of simulations identified concepts directly related to COVID-19 and resistant hypertension or connected via one of three renin\u2013angiotensin\u2013aldosterone system hub nodes (mineralocorticoid receptor, epithelial sodium channel, angiotensin I receptor). The top-ranking concepts relating COVID-19 to resistant hypertension included: cGMP-dependent protein kinase II, MAP3K1, haspin, ral guanine nucleotide exchange factor, N-(3-Oxododecanoyl)-L-homoserine lactone, aspartic endopeptidases, metabotropic glutamate receptors, choline-phosphate cytidylyltransferase, protein tyrosine phosphatase, tat genes, MAP3K10, uridine kinase, dicer enzyme, CMD1B, USP17L2, FLNA, exportin 5, somatotropin releasing hormone, beta-melanocyte stimulating hormone, pegylated leptin, beta-lipoprotein, corticotropin, growth hormone-releasing peptide 2, pro-opiomelanocortin, alpha-melanocyte stimulating hormone, prolactin, thyroid hormone, poly-beta-hydroxybutyrate depolymerase, CR 1392, BCR-ABL fusion gene, high density lipoprotein sphingomyelin, pregnancy-associated murine protein 1, recQ4 helicase, immunoglobulin heavy chain variable domain, aglycotransferrin, host cell factor C1, ATP6V0D1, imipramine demethylase, TRIM40, H3C2 gene, COL1A1+COL1A2 gene, QARS gene, VPS54, TPM2, MPST, EXOSC2, ribosomal protein S10, TAP-144, gonadotropins, human gonadotropin releasing hormone 1, beta-lipotropin, octreotide, salmon calcitonin, des-n-octanoyl ghrelin, liraglutide, gastrins. Concepts were mapped to six physiological themes: altered endocrine function, 23.1%; inflammation or cytokine storm, 21.3%; lipid metabolism and atherosclerosis, 17.6%; sympathetic input to blood pressure regulation, 16.7%; altered entry of COVID-19 virus, 14.8%; and unknown, 6.5%.<\/jats:p>","DOI":"10.3390\/biology12091269","type":"journal-article","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T21:08:36Z","timestamp":1695330516000},"page":"1269","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Literature-Based Discovery to Elucidate the Biological Links between Resistant Hypertension and COVID-19"],"prefix":"10.3390","volume":"12","author":[{"given":"David","family":"Kartchner","sequence":"first","affiliation":[{"name":"Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA"},{"name":"Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3570-6826","authenticated-orcid":false,"given":"Kevin","family":"McCoy","sequence":"additional","affiliation":[{"name":"Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA"},{"name":"Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Janhvi","family":"Dubey","sequence":"additional","affiliation":[{"name":"Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA"},{"name":"Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6405-5728","authenticated-orcid":false,"given":"Dongyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA"},{"name":"Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"Zheng","sequence":"additional","affiliation":[{"name":"Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA"},{"name":"Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rushda","family":"Umrani","sequence":"additional","affiliation":[{"name":"Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA"},{"name":"College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James J.","family":"Kim","sequence":"additional","affiliation":[{"name":"Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5472-6355","authenticated-orcid":false,"given":"Cassie S.","family":"Mitchell","sequence":"additional","affiliation":[{"name":"Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA"},{"name":"Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA"},{"name":"Center for Machine Learning at Georgia Tech, Georgia Institute of Technology, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tang, D., Comish, P., and Kang, R. 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