{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T05:19:11Z","timestamp":1778822351111,"version":"3.51.4"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T00:00:00Z","timestamp":1698624000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T00:00:00Z","timestamp":1698624000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"National Science Foundation","award":["2203236"],"award-info":[{"award-number":["2203236"]}]},{"DOI":"10.13039\/100000005","name":"U.S. Department of Defense","doi-asserted-by":"publisher","award":["w81xwh-21-1-0570"],"award-info":[{"award-number":["w81xwh-21-1-0570"]}],"id":[{"id":"10.13039\/100000005","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>External factors such as exposure to a chemical, drug, or toxicant (CDT), or conversely, the lack of certain chemicals can cause many diseases. The ability to identify such causal CDTs based on changes in the gene expression profile is extremely important in many studies. Furthermore, the ability to correctly infer CDTs that can revert the gene expression changes induced by a given disease phenotype is a crucial step in drug repurposing. We present an approach for Predicting Upstream REgulators (PURE) designed to tackle this challenge. PURE can correctly infer a CDT from the measured expression changes in a given phenotype, as well as correctly identify drugs that could revert disease-induced gene expression changes. We compared the proposed approach with four classical approaches as well as with the causal analysis used in Ingenuity Pathway Analysis (IPA) on 16 data sets (1 rat, 5 mouse, and 10 human data sets), involving 8 chemicals or drugs. We assessed the results based on the ability to correctly identify the CDT as indicated by its rank. We also considered the number of false positives, i.e. CDTs other than the correct CDT that were reported to be significant by each method. The proposed approach performed best in 11 out of the 16 experiments, reporting the correct CDT at the very top 7 times. IPA was the second best, reporting the correct CDT at the top 5 times, but was unable to identify the correct CDT at all in 5 out of the 16 experiments. The validation results showed that our approach, PURE, outperformed some of the most popular methods in the field. PURE could effectively infer the true CDTs responsible for the observed gene expression changes and could also be useful in drug repurposing applications.<\/jats:p>","DOI":"10.1038\/s41598-023-41374-0","type":"journal-article","created":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T11:02:28Z","timestamp":1698663748000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A novel approach for predicting upstream regulators (PURE) that affect gene expression"],"prefix":"10.1038","volume":"13","author":[{"given":"Tuan-Minh","family":"Nguyen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Douglas B.","family":"Craig","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Duc","family":"Tran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tin","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sorin","family":"Draghici","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,30]]},"reference":[{"key":"41374_CR1","doi-asserted-by":"publisher","first-page":"3587","DOI":"10.1093\/bioinformatics\/bti565","volume":"21","author":"P Khatri","year":"2005","unstructured":"Khatri, P. & Draghici, S. Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics 21, 3587\u20133595 (2005).","journal-title":"Bioinformatics"},{"key":"41374_CR2","doi-asserted-by":"publisher","first-page":"e1002375","DOI":"10.1371\/journal.pcbi.1002375","volume":"8","author":"P Khatri","year":"2012","unstructured":"Khatri, P., Sirota, M. & Butte, A. J. Ten years of pathway analysis: Current approaches and outstanding challenges. PLoS Computational Biology\u00a08, e1002375 (2012).","journal-title":"PLoS Computational Biology"},{"key":"41374_CR3","doi-asserted-by":"publisher","first-page":"278","DOI":"10.3389\/fphys.2013.00278","volume":"4","author":"C Mitrea","year":"2013","unstructured":"Mitrea, C. et al. Methods and approaches in the topology-based analysis of biological pathways. Front. Physiol. 4, 278 (2013).","journal-title":"Front. Physiol."},{"key":"41374_CR4","doi-asserted-by":"publisher","first-page":"e79217","DOI":"10.1371\/journal.pone.0079217","volume":"8","author":"AL Tarca","year":"2013","unstructured":"Tarca, A. L., Bhatti, G. & Romero, R. A comparison of gene set analysis methods in terms of sensitivity, prioritization and specificity. PloS One 8, e79217 (2013).","journal-title":"PloS One"},{"key":"41374_CR5","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1002\/cpbi.42","volume":"61","author":"T Nguyen","year":"2018","unstructured":"Nguyen, T., Mitrea, C. & Draghici, S. Network-based approaches for pathway level analysis. Curr. Protoc. Bioinform. 61, 8\u201325 (2018).","journal-title":"Curr. Protoc. Bioinform."},{"key":"41374_CR6","doi-asserted-by":"publisher","first-page":"e0191154","DOI":"10.1371\/journal.pone.0191154","volume":"13","author":"I Ihnatova","year":"2018","unstructured":"Ihnatova, I., Popovici, V. & Budinska, E. A critical comparison of topology-based pathway analysis methods. PLoS ONE 13, e0191154 (2018).","journal-title":"PLoS ONE"},{"key":"41374_CR7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-018-1612-0","volume":"20","author":"T-M Nguyen","year":"2019","unstructured":"Nguyen, T.-M., Shafi, A., Nguyen, T. & Draghici, S. Identifying significantly impacted pathways: A comprehensive review and assessment. Genome Biol. 20, 1\u201315 (2019).","journal-title":"Genome Biol."},{"key":"41374_CR8","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1038\/nrg3552","volume":"14","author":"K Mitra","year":"2013","unstructured":"Mitra, K., Carvunis, A.-R., Ramesh, S. K. & Ideker, T. Integrative approaches for finding modular structure in biological networks. Nat. Rev. Genet. 14, 719\u2013732 (2013).","journal-title":"Nat. Rev. Genet."},{"key":"41374_CR9","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1038\/nrg2363","volume":"9","author":"YS Rhee","year":"2008","unstructured":"Rhee, Y. S., Wood, V., Dolinski, K. & Draghici, S. Use and misuse of the Gene Ontology annotations. Nat. Rev. Genet. 9, 509\u2013515 (2008).","journal-title":"Nat. Rev. Genet."},{"key":"41374_CR10","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1038\/ng1589","volume":"37","author":"EE Schadt","year":"2005","unstructured":"Schadt, E. E. et al. An integrative genomics approach to infer causal associations between gene expression and disease. Nat. Genet. 37, 710\u2013717 (2005).","journal-title":"Nat. Genet."},{"key":"41374_CR11","doi-asserted-by":"publisher","first-page":"1114","DOI":"10.1093\/bioinformatics\/bts090","volume":"28","author":"L Chindelevitch","year":"2012","unstructured":"Chindelevitch, L. et al. Causal reasoning on biological networks: Interpreting transcriptional changes. Bioinformatics 28, 1114\u20131121 (2012).","journal-title":"Bioinformatics"},{"key":"41374_CR12","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1093\/bioinformatics\/btt703","volume":"30","author":"A Kr\u00e4mer","year":"2013","unstructured":"Kr\u00e4mer, A., Green, J., Pollard, J. Jr. & Tugendreich, S. Causal analysis approaches in ingenuity pathway analysis. Bioinformatics 30, 523\u2013530 (2013).","journal-title":"Bioinformatics"},{"key":"41374_CR13","doi-asserted-by":"crossref","unstructured":"Felciano, R.\u00a0M. et\u00a0al. Predictive systems biology approach to broad-spectrum, host-directed drug target discovery in infectious diseases, in Biocomputing 2013, 17\u201328 (World Scientific, 2013).","DOI":"10.1142\/9789814447973_0003"},{"key":"41374_CR14","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1002\/jez.a.307","volume":"305","author":"C Mattingly","year":"2006","unstructured":"Mattingly, C., Rosenstein, M., Colby, G., Forrest, J. Jr. & Boyer, J. The comparative toxicogenomics database (CTD): A resource for comparative toxicological studies. J. Exp. Zool. A Comp. Exp. Biol. 305, 689\u2013692 (2006).","journal-title":"J. Exp. Zool. A Comp. Exp. Biol."},{"key":"41374_CR15","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1093\/nar\/28.1.27","volume":"28","author":"M Kanehisa","year":"2000","unstructured":"Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27\u201330 (2000).","journal-title":"Nucleic Acids Res."},{"key":"41374_CR16","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1093\/nar\/gkt1068","volume":"42","author":"V Law","year":"2014","unstructured":"Law, V. et al. Drugbank 4.0: Shedding new light on drug metabolism. Nucleic Acids Res. 42, 1091\u20131097 (2014).","journal-title":"Nucleic Acids Res."},{"key":"41374_CR17","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1089\/dia.2005.7.323","volume":"7","author":"J Pollard Jr","year":"2005","unstructured":"Pollard, J. Jr. et al. A computational model to define the molecular causes of type 2 diabetes mellitus. Diabetes Technol. Ther. 7, 323\u2013336 (2005).","journal-title":"Diabetes Technol. Ther."},{"key":"41374_CR18","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/S0888-7543(02)00021-6","volume":"81","author":"S Draghici","year":"2003","unstructured":"Draghici, S., Khatri, P., Martins, R. P., Ostermeier, G. C. & Krawetz, S. A. Global functional profiling of gene expression. Genomics 81, 98\u2013104 (2003).","journal-title":"Genomics"},{"key":"41374_CR19","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1080\/01621459.1951.10500769","volume":"46","author":"FJ Massey Jr","year":"1951","unstructured":"Massey, F. J. Jr. The Kolmogorov-Smirnov test for goodness of fit. J. Am. Stat. Assoc. 46, 68\u201378 (1951).","journal-title":"J. Am. Stat. Assoc."},{"key":"41374_CR20","doi-asserted-by":"publisher","first-page":"80","DOI":"10.2307\/3001968","volume":"1","author":"F Wilcoxon","year":"1945","unstructured":"Wilcoxon, F. Individual comparisons by ranking methods. Biometrics 1, 80\u201383 (1945).","journal-title":"Biometrics"},{"key":"41374_CR21","unstructured":"Korotkevich, G. et\u00a0al. Fast gene set enrichment analysis. BioRxiv 060012 (2021)."},{"key":"41374_CR22","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1038\/448645a","volume":"448","author":"CR Chong","year":"2007","unstructured":"Chong, C. R. & Sullivan, D. J. New uses for old drugs. Nature 448, 645\u2013646 (2007).","journal-title":"Nature"},{"key":"41374_CR23","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1016\/S1471-4892(03)00110-3","volume":"3","author":"KJ Shaw","year":"2003","unstructured":"Shaw, K. J. & Morrow, B. J. Transcriptional profiling and drug discovery. Curr. Opin. Pharmacol. 3, 508\u2013512 (2003).","journal-title":"Curr. Opin. Pharmacol."},{"key":"41374_CR24","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1016\/j.drudis.2012.07.014","volume":"18","author":"F Iorio","year":"2013","unstructured":"Iorio, F., Rittman, T., Ge, H., Menden, M. & Saez-Rodriguez, J. Transcriptional data: A new gateway to drug repositioning?. Drug Discov. Today 18, 350\u2013357 (2013).","journal-title":"Drug Discov. Today"},{"key":"41374_CR25","doi-asserted-by":"publisher","first-page":"96ra77","DOI":"10.1126\/scitranslmed.3001318","volume":"3","author":"M Sirota","year":"2011","unstructured":"Sirota, M. et al. Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci. Transl. Med. 3, 96ra77 (2011).","journal-title":"Sci. Transl. Med."},{"key":"41374_CR26","doi-asserted-by":"publisher","first-page":"e16382","DOI":"10.1371\/journal.pone.0016382","volume":"6","author":"DG McArt","year":"2011","unstructured":"McArt, D. G. & Zhang, S.-D. Identification of candidate small-molecule therapeutics to cancer by gene-signature perturbation in connectivity mapping. PloS One 6, e16382 (2011).","journal-title":"PloS One"},{"key":"41374_CR27","doi-asserted-by":"publisher","first-page":"e24662","DOI":"10.1371\/journal.pone.0024662","volume":"6","author":"S Claerhout","year":"2011","unstructured":"Claerhout, S. et al. Gene expression signature analysis identifies vorinostat as a candidate therapy for gastric cancer. PloS one 6, e24662 (2011).","journal-title":"PloS one"},{"key":"41374_CR28","doi-asserted-by":"publisher","first-page":"e27186","DOI":"10.1371\/journal.pone.0027186","volume":"6","author":"M-H Chen","year":"2011","unstructured":"Chen, M.-H. et al. Gene expression-based chemical genomics identifies potential therapeutic drugs in hepatocellular carcinoma. PloS one 6, e27186 (2011).","journal-title":"PloS one"},{"key":"41374_CR29","doi-asserted-by":"publisher","first-page":"96ra76","DOI":"10.1126\/scitranslmed.3002648","volume":"3","author":"JT Dudley","year":"2011","unstructured":"Dudley, J. T. et al. Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci. Transl. Med. 3, 96ra76 (2011).","journal-title":"Sci. Transl. Med."},{"key":"41374_CR30","doi-asserted-by":"publisher","first-page":"1204","DOI":"10.4161\/auto.6.8.13551","volume":"6","author":"F Iorio","year":"2010","unstructured":"Iorio, F., Isacchi, A., di Bernardo, D. & Brunetti-Pierri, N. Identification of small molecules enhancing autophagic function from drug network analysis. Autophagy 6, 1204\u20131205 (2010).","journal-title":"Autophagy"},{"key":"41374_CR31","doi-asserted-by":"publisher","first-page":"14621","DOI":"10.1073\/pnas.1000138107","volume":"107","author":"F Iorio","year":"2010","unstructured":"Iorio, F. et al. Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc. Natl. Acad. Sci. 107, 14621\u201314626 (2010).","journal-title":"Proc. Natl. Acad. Sci."},{"key":"41374_CR32","doi-asserted-by":"publisher","first-page":"E771","DOI":"10.1073\/pnas.1106149108","volume":"108","author":"AJ Wolpaw","year":"2011","unstructured":"Wolpaw, A. J. et al. Modulatory profiling identifies mechanisms of small molecule-induced cell death. Proc. Natl. Acad. Sci. 108, E771\u2013E780 (2011).","journal-title":"Proc. Natl. Acad. Sci."},{"key":"41374_CR33","doi-asserted-by":"publisher","first-page":"e6536","DOI":"10.1371\/journal.pone.0006536","volume":"4","author":"G Hu","year":"2009","unstructured":"Hu, G. & Agarwal, P. Human disease-drug network based on genomic expression profiles. PloS one 4, e6536 (2009).","journal-title":"PloS one"},{"key":"41374_CR34","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1038\/clpt.2009.103","volume":"86","author":"AP Chiang","year":"2009","unstructured":"Chiang, A. P. & Butte, A. J. Systematic evaluation of drug-disease relationships to identify leads for novel drug uses. Clin. Pharmacol. Ther. 86, 507\u2013510 (2009).","journal-title":"Clin. Pharmacol. Ther."},{"key":"41374_CR35","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1038\/nrc2044","volume":"7","author":"J Lamb","year":"2007","unstructured":"Lamb, J. The connectivity map: A new tool for biomedical research. Nat. Rev. Cancer 7, 54\u201360 (2007).","journal-title":"Nat. Rev. Cancer"},{"key":"41374_CR36","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.1126\/science.1132939","volume":"313","author":"J Lamb","year":"2006","unstructured":"Lamb, J. et al. The connectivity map: Using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929\u20131935 (2006).","journal-title":"Science"},{"key":"41374_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1755-8794-6-S3-S4","volume":"6","author":"Y-F Huang","year":"2013","unstructured":"Huang, Y.-F., Yeh, H.-Y. & Soo, V.-W. Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation. BMC Med. Genomics 6, 1\u201314 (2013).","journal-title":"BMC Med. Genomics"},{"key":"41374_CR38","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1111\/1467-9469.00072","volume":"24","author":"Y Benjamini","year":"1997","unstructured":"Benjamini, Y. & Hochberg, Y. Multiple hypotheses testing with weights. Scand. J. Stat. 24, 407\u2013418 (1997).","journal-title":"Scand. J. Stat."},{"key":"41374_CR39","doi-asserted-by":"publisher","first-page":"15545","DOI":"10.1073\/pnas.0506580102","volume":"102","author":"A Subramanian","year":"2005","unstructured":"Subramanian, A. et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U. S. Am. 102, 15545\u201315550 (2005).","journal-title":"Proc. Natl. Acad. Sci. U. S. Am."},{"key":"41374_CR40","unstructured":"Wilson, K.\u00a0C., Chotirmall, S.\u00a0H., Bai, C. & Rello, J. COVID-19: Interim guidance on management pending empirical evidence. From Am. Thorac. Soc.-led Int. Task Force 12 (2020)."},{"issue":"17","key":"41374_CR41","doi-asserted-by":"publisher","first-page":"2691","DOI":"10.1093\/bioinformatics\/btab163","volume":"37","author":"S Draghici","year":"2021","unstructured":"Draghici, S. et al. COVID-19: Disease pathways and gene expression changes predict methylprednisolone can improve outcome in severe cases. Bioinformatics 37(17), 2691\u20132698 (2021).","journal-title":"Bioinformatics"},{"key":"41374_CR42","doi-asserted-by":"crossref","unstructured":"Meduri, G.\u00a0U. et\u00a0al. Pharmacological principles guiding prolonged glucocorticoid treatment in ards. Intensive Care Med. 1\u201313 (2020).","DOI":"10.1007\/s00134-020-06289-8"},{"key":"41374_CR43","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s00508-020-01805-8","volume":"133","author":"L Corral-Gudino","year":"2021","unstructured":"Corral-Gudino, L. et al. Methylprednisolone in adults hospitalized with COVID-19 pneumonia: An open-label randomized trial (GLUCOCOVID). Wien. Klin. Wochenschr. 133, 303\u2013311 (2021).","journal-title":"Wien. Klin. Wochenschr."},{"key":"41374_CR44","unstructured":"Salton, F. et\u00a0al. Prolonged low-dose methylprednisolone in patients with severe COVID-19 pneumonia. In Open forum infectious diseases, vol.\u00a07, ofaa421 (Oxford University Press US, 2020)."},{"key":"41374_CR45","doi-asserted-by":"crossref","unstructured":"Group, C.\u00a0H. et\u00a0al. Systemic corticosteroids for the treatment of COVID-19. Cochrane Database Syst. Rev. 2021 (1996).","DOI":"10.1002\/14651858.CD014963"},{"key":"41374_CR46","doi-asserted-by":"publisher","first-page":"1292","DOI":"10.1001\/jama.2020.16747","volume":"324","author":"HC Prescott","year":"2020","unstructured":"Prescott, H. C. & Rice, T. W. Corticosteroids in covid-19 ards: Evidence and hope during the pandemic. JAMA 324, 1292\u20131295 (2020).","journal-title":"JAMA"},{"key":"41374_CR47","doi-asserted-by":"publisher","first-page":"4021","DOI":"10.1074\/jbc.M606262200","volume":"282","author":"O Stojadinovic","year":"2007","unstructured":"Stojadinovic, O. et al. Novel genomic effects of glucocorticoids in epidermal keratinocytes: Inhibition of apoptosis, interferon-$$\\gamma$$ pathway, and wound healing along with promotion of terminal differentiation. J. Biol. Chem. 282, 4021\u20134034 (2007).","journal-title":"J. Biol. Chem."},{"key":"41374_CR48","doi-asserted-by":"publisher","first-page":"2611","DOI":"10.1128\/MCB.01121-13","volume":"34","author":"ME Peffer","year":"2014","unstructured":"Peffer, M. E. et al. Caveolin-1 regulates genomic action of the glucocorticoid receptor in neural stem cells. Mol. Cell. Biol. 34, 2611\u20132623 (2014).","journal-title":"Mol. Cell. Biol."},{"key":"41374_CR49","doi-asserted-by":"publisher","first-page":"e0170441","DOI":"10.1371\/journal.pone.0170441","volume":"12","author":"L Stenz","year":"2017","unstructured":"Stenz, L., Escoffier, J., Rahban, R., Nef, S. & Paoloni-Giacobino, A. Testicular dysgenesis syndrome and long-lasting epigenetic silencing of mouse sperm genes involved in the reproductive system after prenatal exposure to dehp. PloS one 12, e0170441 (2017).","journal-title":"PloS one"},{"key":"41374_CR50","doi-asserted-by":"publisher","first-page":"e0134057","DOI":"10.1371\/journal.pone.0134057","volume":"10","author":"BE Himes","year":"2015","unstructured":"Himes, B. E. et al. Vitamin D modulates expression of the airway smooth muscle transcriptome in fatal asthma. PloS One 10, e0134057 (2015).","journal-title":"PloS One"},{"key":"41374_CR51","doi-asserted-by":"publisher","first-page":"e87","DOI":"10.1371\/journal.pgen.0030087","volume":"3","author":"C-Y Lin","year":"2007","unstructured":"Lin, C.-Y. et al. Whole-genome cartography of estrogen receptor $$\\alpha$$ binding sites. PLoS Genet. 3, e87 (2007).","journal-title":"PLoS Genet."},{"key":"41374_CR52","doi-asserted-by":"publisher","first-page":"1052","DOI":"10.1016\/j.dib.2016.03.069","volume":"7","author":"RM Rodrigues","year":"2016","unstructured":"Rodrigues, R. M. et al. Gene expression data from acetaminophen-induced toxicity in human hepatic in vitro systems and clinical liver samples. Data Brief 7, 1052\u20131057 (2016).","journal-title":"Data Brief"},{"key":"41374_CR53","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1007\/s00109-006-0146-1","volume":"85","author":"P Hanifi-Moghaddam","year":"2007","unstructured":"Hanifi-Moghaddam, P. et al. Molecular analysis of human endometrium: Short-term tibolone signaling differs significantly from estrogen and estrogen plus progestagen signaling. J. Mol. Med. 85, 471\u2013480. https:\/\/doi.org\/10.1007\/s00109-006-0146-1 (2007).","journal-title":"J. Mol. Med."},{"key":"41374_CR54","doi-asserted-by":"publisher","first-page":"996","DOI":"10.1016\/j.jnutbio.2015.04.009","volume":"26","author":"S Tallino","year":"2015","unstructured":"Tallino, S. et al. Nutrigenomics analysis reveals that copper deficiency and dietary sucrose up-regulate inflammation, fibrosis and lipogenic pathways in a mature rat model of nonalcoholic fatty liver disease. J. Nutr. Biochem. 26, 996\u20131006 (2015).","journal-title":"J. Nutr. Biochem."},{"key":"41374_CR55","doi-asserted-by":"publisher","first-page":"1036","DOI":"10.1016\/j.cell.2020.04.026","volume":"181","author":"D Blanco-Melo","year":"2020","unstructured":"Blanco-Melo, D. et al. Imbalanced host response to SARS-CoV-2 drives development of COVID-19. Cell 181, 1036\u20131045. https:\/\/doi.org\/10.1016\/j.cell.2020.04.026 (2020).","journal-title":"Cell"}],"updated-by":[{"DOI":"10.1038\/s41598-024-64281-4","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T00:00:00Z","timestamp":1717977600000}}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-023-41374-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-023-41374-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-023-41374-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T13:05:50Z","timestamp":1718024750000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-023-41374-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,30]]},"references-count":55,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["41374"],"URL":"https:\/\/doi.org\/10.1038\/s41598-023-41374-0","relation":{"correction":[{"id-type":"doi","id":"10.1038\/s41598-024-64281-4","asserted-by":"object"}]},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,30]]},"assertion":[{"value":"6 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 August 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 October 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 June 2024","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1038\/s41598-024-64281-4","URL":"https:\/\/doi.org\/10.1038\/s41598-024-64281-4","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"SD is the founder of Advaita Bioinformatics which is commercializing iPathwayGuide, which is an IPA alternative. The method proposed in this article is not commercialized by AdvaitaBio. No funding, salaries, equipment or supplies have been received from Advaita for this work. No consultation fees, in kind, or any other forms of remuneration were received for this work.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"18571"}}