{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T04:05:05Z","timestamp":1746763505437,"version":"3.40.5"},"reference-count":75,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T00:00:00Z","timestamp":1746662400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T00:00:00Z","timestamp":1746662400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Netw Model Anal Health Inform Bioinforma"],"DOI":"10.1007\/s13721-025-00525-1","type":"journal-article","created":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T17:03:04Z","timestamp":1746723784000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Identification of potential candidate genes in rheumatoid arthritis using integrated machine learning and WGCNA approach on transcriptomic data"],"prefix":"10.1007","volume":"14","author":[{"given":"Haseeb","family":"Nisar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Komal","family":"Javed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Areej","family":"Arshad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hamna","family":"Habib","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kashif Iqbal","family":"Sahibzada","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samiah","family":"Shahid","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,8]]},"reference":[{"key":"525_CR1","doi-asserted-by":"crossref","unstructured":"Anders S, Huber W (2010) Differential expression analysis for sequence count data. Nat Proc","DOI":"10.1038\/npre.2010.4282.2"},{"key":"525_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-4-2","volume":"4","author":"GD Bader","year":"2003","unstructured":"Bader GD, Hogue CWV (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinform 4:1\u201327","journal-title":"BMC Bioinform"},{"issue":"2","key":"525_CR3","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1093\/rheumatology\/keq344","volume":"50","author":"C Bansard","year":"2011","unstructured":"Bansard C et al (2011) Gene profiling predicts rheumatoid arthritis responsiveness to IL-1Ra (anakinra). Rheumatology 50(2):283\u2013292. https:\/\/doi.org\/10.1093\/rheumatology\/keq344","journal-title":"Rheumatology"},{"issue":"1","key":"525_CR4","doi-asserted-by":"publisher","DOI":"10.1136\/rmdopen-2020-001242","volume":"6","author":"SA Bergstra","year":"2020","unstructured":"Bergstra SA et al (2020) Earlier is better when treating rheumatoid arthritis: but can we detect a window of opportunity? RMD Open 6(1):e001242","journal-title":"RMD Open"},{"issue":"2","key":"525_CR5","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1039\/B918972F","volume":"135","author":"RG Brereton","year":"2010","unstructured":"Brereton RG, Lloyd GR (2010) Support vector machines for classification and regression. Analyst 135(2):230\u2013267","journal-title":"Analyst"},{"issue":"1","key":"525_CR6","doi-asserted-by":"publisher","DOI":"10.1136\/rmdopen-2018-000870","volume":"5","author":"LE Burgers","year":"2019","unstructured":"Burgers LE, Raza K, Van Der Helm-Van AH (2019) Window of opportunity in rheumatoid arthritis\u2013definitions and supporting evidence: from old to new perspectives. RMD Open 5(1):e000870","journal-title":"RMD Open"},{"issue":"4","key":"525_CR7","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1007\/s00423-008-0317-3","volume":"393","author":"K Buttenschoen","year":"2008","unstructured":"Buttenschoen K et al (2008) Endotoxemia and endotoxin tolerance in patients with ARDS. Langenbecks Arch Surg 393(4):473\u2013478. https:\/\/doi.org\/10.1007\/s00423-008-0317-3","journal-title":"Langenbecks Arch Surg"},{"issue":"1","key":"525_CR8","doi-asserted-by":"publisher","first-page":"14071","DOI":"10.1038\/s41598-020-70832-2","volume":"10","author":"S Cascianelli","year":"2020","unstructured":"Cascianelli S et al (2020) Machine learning for RNA sequencing-based intrinsic subtyping of breast cancer. Sci Rep 10(1):14071. https:\/\/doi.org\/10.1038\/s41598-020-70832-2","journal-title":"Sci Rep"},{"key":"525_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1752-0509-8-S4-S11","volume":"8","author":"C-H Chin","year":"2014","unstructured":"Chin C-H et al (2014) cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 8:1\u20137","journal-title":"BMC Syst Biol"},{"issue":"1","key":"525_CR10","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1186\/s13059-016-0881-8","volume":"17","author":"A Conesa","year":"2016","unstructured":"Conesa A et al (2016) A survey of best practices for RNA-seq data analysis. Genome Biol 17(1):13. https:\/\/doi.org\/10.1186\/s13059-016-0881-8","journal-title":"Genome Biol"},{"issue":"2","key":"525_CR11","first-page":"224","volume":"22","author":"A Crilly","year":"1995","unstructured":"Crilly A et al (1995) Interleukin 6 (IL-6) and soluble IL-2 receptor levels in patients with rheumatoid arthritis treated with low dose oral methotrexate. J Rheumatol 22(2):224\u2013226","journal-title":"J Rheumatol"},{"issue":"2","key":"525_CR12","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1124\/pr.57.2.3","volume":"57","author":"BN Cronstein","year":"2005","unstructured":"Cronstein BN (2005) Low-dose methotrexate: a mainstay in the treatment of rheumatoid arthritis. Pharmacol Rev 57(2):163\u2013172. https:\/\/doi.org\/10.1124\/pr.57.2.3","journal-title":"Pharmacol Rev"},{"issue":"1","key":"525_CR13","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1186\/s12859-016-1208-1","volume":"17","author":"K Dong","year":"2016","unstructured":"Dong K et al (2016) NBLDA: negative binomial linear discriminant analysis for RNA-Seq data. BMC Bioinformatics 17(1):369. https:\/\/doi.org\/10.1186\/s12859-016-1208-1","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"525_CR14","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1186\/2045-3701-2-26","volume":"2","author":"Z Fang","year":"2012","unstructured":"Fang Z, Martin J, Wang Z (2012) Statistical methods for identifying differentially expressed genes in RNA-Seq experiments. Cell Biosci 2(1):26. https:\/\/doi.org\/10.1186\/2045-3701-2-26","journal-title":"Cell Biosci"},{"key":"525_CR15","doi-asserted-by":"publisher","first-page":"951582","DOI":"10.3389\/fimmu.2022.951582","volume":"13","author":"S Feng","year":"2022","unstructured":"Feng S et al (2022) Integrative analysis from multicenter studies identifies a WGCNA-derived cancer-associated fibroblast signature for ovarian cancer. Front Immunol 13:951582. https:\/\/doi.org\/10.3389\/fimmu.2022.951582","journal-title":"Front Immunol"},{"issue":"3","key":"525_CR16","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1111\/j.1365-2249.2008.03634.x","volume":"152","author":"LI Filippin","year":"2008","unstructured":"Filippin LI et al (2008) Redox signalling and the inflammatory response in rheumatoid arthritis. Clin Exp Immunol 152(3):415\u2013422. https:\/\/doi.org\/10.1111\/j.1365-2249.2008.03634.x","journal-title":"Clin Exp Immunol"},{"issue":"2","key":"525_CR17","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1002\/art.1780370207","volume":"37","author":"GS Firestein","year":"1994","unstructured":"Firestein GS, Paine MM, Boyle DL (1994) Mechanisms of methotrexate action in rheumatoid arthritis. Arthritis Rheum 37(2):193\u2013200. https:\/\/doi.org\/10.1002\/art.1780370207","journal-title":"Arthritis Rheum"},{"key":"525_CR18","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.cmpb.2019.04.007","volume":"175","author":"D Goksuluk","year":"2019","unstructured":"Goksuluk D et al (2019) MLSeq: Machine learning interface for RNA-sequencing data. Comput Methods Programs Biomed 175:223\u2013231. https:\/\/doi.org\/10.1016\/j.cmpb.2019.04.007","journal-title":"Comput Methods Programs Biomed"},{"issue":"8","key":"525_CR19","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1038\/cgt.2017.23","volume":"24","author":"X Guo","year":"2017","unstructured":"Guo X et al (2017) Identification of breast cancer mechanism based on weighted gene coexpression network analysis. Cancer Gene Ther 24(8):333\u2013341. https:\/\/doi.org\/10.1038\/cgt.2017.23","journal-title":"Cancer Gene Ther"},{"key":"525_CR20","unstructured":"Hastie T, et al (2013) 24 pamr. predict. Package \u2018pamr\u2019, p 24"},{"issue":"2","key":"525_CR21","doi-asserted-by":"publisher","first-page":"81","DOI":"10.2217\/epi-2021-0318","volume":"14","author":"Z He","year":"2022","unstructured":"He Z et al (2022) Comprehensive analysis of epigenetic modifications and immune-cell infiltration in tissues from patients with systemic lupus erythematosus. Epigenomics 14(2):81\u2013100","journal-title":"Epigenomics"},{"issue":"1","key":"525_CR22","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1186\/s13045-020-01005-x","volume":"13","author":"M Hong","year":"2020","unstructured":"Hong M et al (2020) RNA sequencing: new technologies and applications in cancer research. J Hematol Oncol 13(1):166. https:\/\/doi.org\/10.1186\/s13045-020-01005-x","journal-title":"J Hematol Oncol"},{"issue":"3","key":"525_CR23","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1016\/j.immuni.2018.01.014","volume":"48","author":"B Johnson","year":"2018","unstructured":"Johnson B et al (2018) Human IFIT3 modulates IFIT1 RNA binding specificity and protein stability. Immunity 48(3):487-499.e5. https:\/\/doi.org\/10.1016\/j.immuni.2018.01.014","journal-title":"Immunity"},{"issue":"6","key":"525_CR24","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1038\/icb.2017.16","volume":"95","author":"A Kan","year":"2017","unstructured":"Kan A (2017) Machine learning applications in cell image analysis. Immunol Cell Biol 95(6):525\u2013530","journal-title":"Immunol Cell Biol"},{"issue":"2","key":"525_CR25","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1080\/25785826.2020.1751908","volume":"43","author":"M Kato","year":"2020","unstructured":"Kato M (2020) New insights into IFN-\u03b3 in rheumatoid arthritis: role in the era of JAK inhibitors. Immunol Med 43(2):72\u201378","journal-title":"Immunol Med"},{"key":"525_CR26","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.csbj.2014.11.005","volume":"13","author":"K Kourou","year":"2015","unstructured":"Kourou K et al (2015) Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J 13:8\u201317","journal-title":"Comput Struct Biotechnol J"},{"key":"525_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-018-0285-8","volume":"10","author":"R Kriv\u00e1k","year":"2018","unstructured":"Kriv\u00e1k R, Hoksza D (2018) P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure. J Cheminform 10:1\u201312","journal-title":"J Cheminform"},{"key":"525_CR28","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.biopha.2016.02.001","volume":"79","author":"LD Kumar","year":"2016","unstructured":"Kumar LD et al (2016) Advancement in contemporary diagnostic and therapeutic approaches for rheumatoid arthritis. Biomed Pharmacother 79:52\u201361","journal-title":"Biomed Pharmacother"},{"issue":"6","key":"525_CR29","doi-asserted-by":"publisher","first-page":"1072","DOI":"10.1002\/ibd.21887","volume":"18","author":"C Labb\u00e9","year":"2012","unstructured":"Labb\u00e9 C et al (2012) Genome-wide expression profiling implicates a MAST3-regulated gene set in colonic mucosal inflammation of ulcerative colitis patients. Inflamm Bowel Dis 18(6):1072\u20131080","journal-title":"Inflamm Bowel Dis"},{"issue":"9","key":"525_CR30","doi-asserted-by":"publisher","first-page":"1440","DOI":"10.1136\/ard.2008.093146","volume":"68","author":"C Landolt-Marticorena","year":"2009","unstructured":"Landolt-Marticorena C et al (2009) Lack of association between the interferon-\u03b1 signature and longitudinal changes in disease activity in systemic lupus erythematosus. Ann Rheum Dis 68(9):1440\u20131446","journal-title":"Ann Rheum Dis"},{"issue":"1","key":"525_CR31","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1186\/1471-2105-9-559","volume":"9","author":"P Langfelder","year":"2008","unstructured":"Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9(1):559. https:\/\/doi.org\/10.1186\/1471-2105-9-559","journal-title":"BMC Bioinformatics"},{"key":"525_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.meomic.2020.100001","volume":"1","author":"NQK Le","year":"2021","unstructured":"Le NQK et al (2021) Identification of gene expression signatures for psoriasis classification using machine learning techniques. Med Omics 1:100001","journal-title":"Med Omics"},{"key":"525_CR33","unstructured":"Lewis RJ (2000) An introduction to classification and regression tree (CART) analysis. Citeseer"},{"issue":"6","key":"525_CR34","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/j.cels.2015.12.004","volume":"1","author":"A Liberzon","year":"2015","unstructured":"Liberzon A et al (2015) The molecular signatures database (MSigDB) hallmark gene set collection. Cell Syst 1(6):417\u2013425. https:\/\/doi.org\/10.1016\/j.cels.2015.12.004","journal-title":"Cell Syst"},{"issue":"12","key":"525_CR35","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1186\/s13059-014-0550-8","volume":"15","author":"MI Love","year":"2014","unstructured":"Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12):550. https:\/\/doi.org\/10.1186\/s13059-014-0550-8","journal-title":"Genome Biol"},{"issue":"2","key":"525_CR36","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1002\/hep.31065","volume":"72","author":"CL Mack","year":"2020","unstructured":"Mack CL et al (2020) Diagnosis and management of autoimmune hepatitis in adults and children: 2019 practice guidance and guidelines from the American Association for the Study of Liver Diseases. Hepatology 72(2):671\u2013722","journal-title":"Hepatology"},{"issue":"10","key":"525_CR37","doi-asserted-by":"publisher","first-page":"712","DOI":"10.15520\/jcmro.v3i10.336","volume":"3","author":"F Meng","year":"2020","unstructured":"Meng F et al (2020) Network pharmacology-based study of active ingredients and mechanisms of compound xuanju for rheumatoid arthritis. J Curr Med Res Opin 3(10):712\u2013723","journal-title":"J Curr Med Res Opin"},{"issue":"1","key":"525_CR38","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1093\/nar\/gkg034","volume":"31","author":"C Mering","year":"2003","unstructured":"Mering C et al (2003) STRING: a database of predicted functional associations between proteins. Nucl Acids Res 31(1):258\u2013261","journal-title":"Nucl Acids Res"},{"issue":"5","key":"525_CR39","doi-asserted-by":"publisher","first-page":"690","DOI":"10.1002\/art.40428","volume":"70","author":"DE Orange","year":"2018","unstructured":"Orange DE et al (2018) Identification of three rheumatoid arthritis disease subtypes by machine learning integration of synovial histologic features and RNA sequencing data. Arthritis Rheumatol 70(5):690\u2013701. https:\/\/doi.org\/10.1002\/art.40428","journal-title":"Arthritis Rheumatol"},{"issue":"13","key":"525_CR40","doi-asserted-by":"publisher","first-page":"1605","DOI":"10.1002\/jcc.20084","volume":"25","author":"EF Pettersen","year":"2004","unstructured":"Pettersen EF et al (2004) UCSF Chimera\u2014a visualization system for exploratory research and analysis. J Comput Chem 25(13):1605\u20131612","journal-title":"J Comput Chem"},{"issue":"17","key":"525_CR41","doi-asserted-by":"publisher","first-page":"3232","DOI":"10.1038\/s41388-018-0662-9","volume":"38","author":"VK Pidugu","year":"2019","unstructured":"Pidugu VK et al (2019) IFIT1 and IFIT3 promote oral squamous cell carcinoma metastasis and contribute to the anti-tumor effect of gefitinib via enhancing p-EGFR recycling. Oncogene 38(17):3232\u20133247","journal-title":"Oncogene"},{"issue":"5","key":"525_CR42","doi-asserted-by":"publisher","first-page":"678","DOI":"10.1002\/art.40810","volume":"71","author":"D Plant","year":"2019","unstructured":"Plant D et al (2019) Profiling of gene expression biomarkers as a classifier of methotrexate nonresponse in patients with rheumatoid arthritis. Arthritis Rheumatol 71(5):678\u2013684","journal-title":"Arthritis Rheumatol"},{"issue":"Suppl 7","key":"525_CR43","doi-asserted-by":"publisher","first-page":"S132","DOI":"10.1186\/1753-6561-3-s7-s132","volume":"3","author":"B Qiao","year":"2009","unstructured":"Qiao B et al (2009) Genome-wide gene-based analysis of rheumatoid arthritis-associated interaction with PTPN22 and HLA-DRB1. BMC Proc 3(Suppl 7):S132. https:\/\/doi.org\/10.1186\/1753-6561-3-s7-s132","journal-title":"BMC Proc"},{"key":"525_CR44","doi-asserted-by":"publisher","DOI":"10.1101\/636340","author":"T Rahman","year":"2019","unstructured":"Rahman T et al (2019) A sparse negative binomial classifier with covariate adjustment for RNA-seq data. BioRxiv. https:\/\/doi.org\/10.1101\/636340","journal-title":"BioRxiv"},{"issue":"5","key":"525_CR45","doi-asserted-by":"publisher","first-page":"bbaa365","DOI":"10.1093\/bib\/bbaa365","volume":"22","author":"MH Rahman","year":"2021","unstructured":"Rahman MH et al (2021) Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression. Brief Bioinform 22(5):bbaa365","journal-title":"Brief Bioinform"},{"issue":"1","key":"525_CR46","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1186\/1471-2105-14-370","volume":"14","author":"H Rehrauer","year":"2013","unstructured":"Rehrauer H et al (2013) Blind spots of quantitative RNA-seq: the limits for assessing abundance, differential expression, and isoform switching. BMC Bioinformatics 14(1):370. https:\/\/doi.org\/10.1186\/1471-2105-14-370","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"525_CR47","doi-asserted-by":"publisher","first-page":"31","DOI":"10.17849\/insm-47-01-31-39.1","volume":"47","author":"SJ Rigatti","year":"2017","unstructured":"Rigatti SJ (2017) Random forest. J Insur Med 47(1):31\u201339","journal-title":"J Insur Med"},{"key":"525_CR48","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2021.638066","volume":"12","author":"D Rychkov","year":"2021","unstructured":"Rychkov D et al (2021) Cross-tissue transcriptomic analysis leveraging machine learning approaches identifies new biomarkers for rheumatoid arthritis. Front Immunol 12:638066","journal-title":"Front Immunol"},{"issue":"4","key":"525_CR49","doi-asserted-by":"publisher","first-page":"btad192","DOI":"10.1093\/bioinformatics\/btad192","volume":"39","author":"E Sciacca","year":"2023","unstructured":"Sciacca E et al (2023) (2023) DEGGs: an R package with shiny app for the identification of differentially expressed gene\u2013gene interactions in high-throughput sequencing data. Bioinformatics 39(4):btad192. https:\/\/doi.org\/10.1093\/bioinformatics\/btad192","journal-title":"Bioinformatics"},{"key":"525_CR50","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.gene.2018.01.033","volume":"647","author":"T Sevimoglu","year":"2018","unstructured":"Sevimoglu T et al (2018) Systems biomarkers in psoriasis: integrative evaluation of computational and experimental data at transcript and protein levels. Gene 647:157\u2013163","journal-title":"Gene"},{"key":"525_CR51","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1007\/s00296-017-3732-3","volume":"37","author":"X Song","year":"2017","unstructured":"Song X, Lin Q (2017) Genomics, transcriptomics and proteomics to elucidate the pathogenesis of rheumatoid arthritis. Rheumatol Int 37:1257\u20131265","journal-title":"Rheumatol Int"},{"key":"525_CR52","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.ijcard.2020.12.007","volume":"328","author":"X Song","year":"2021","unstructured":"Song X et al (2021) Identification of risk genes related to myocardial infarction and the construction of early SVM diagnostic model. Int J Cardiol 328:182\u2013190. https:\/\/doi.org\/10.1016\/j.ijcard.2020.12.007","journal-title":"Int J Cardiol"},{"issue":"11","key":"525_CR53","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1038\/s41576-019-0150-2","volume":"20","author":"R Stark","year":"2019","unstructured":"Stark R, Grzelak M, Hadfield J (2019) RNA sequencing: the teenage years. Nat Rev Genet 20(11):631\u2013656. https:\/\/doi.org\/10.1038\/s41576-019-0150-2","journal-title":"Nat Rev Genet"},{"issue":"12","key":"525_CR54","doi-asserted-by":"publisher","first-page":"1298","DOI":"10.1093\/rheumatology\/36.12.1298","volume":"36","author":"RH Straub","year":"1997","unstructured":"Straub RH et al (1997) Decrease of interleukin 6 during the first 12 months is a prognostic marker for clinical outcome during 36 months treatment with disease-modifying anti-rheumatic drugs. Br J Rheumatol 36(12):1298\u20131303. https:\/\/doi.org\/10.1093\/rheumatology\/36.12.1298","journal-title":"Br J Rheumatol"},{"issue":"1","key":"525_CR55","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1002\/0471250953.bi0813s47","volume":"47","author":"G Su","year":"2014","unstructured":"Su G et al (2014) Biological network exploration with Cytoscape 3. Curr Protoc Bioinform 47(1):8\u201313","journal-title":"Curr Protoc Bioinform"},{"issue":"45","key":"525_CR56","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000031522","volume":"101","author":"E Suzuki","year":"2022","unstructured":"Suzuki E et al (2022) The expression of Ets-1 and Fli-1 is associated with interferon-inducible genes in peripheral blood mononuclear cells from Japanese patients with systemic lupus erythematosus. Medicine 101(45):e31522","journal-title":"Medicine"},{"issue":"2","key":"525_CR57","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1002\/jcc.21334","volume":"31","author":"O Trott","year":"2010","unstructured":"Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455\u2013461","journal-title":"J Comput Chem"},{"issue":"5","key":"525_CR58","doi-asserted-by":"publisher","first-page":"806","DOI":"10.1136\/annrheumdis-2014-206047","volume":"74","author":"JAB van Nies","year":"2015","unstructured":"van Nies JAB et al (2015) Evaluating relationships between symptom duration and persistence of rheumatoid arthritis: does a window of opportunity exist? Results on the Leiden early arthritis clinic and ESPOIR cohorts. Ann Rheum Dis 74(5):806\u2013812","journal-title":"Ann Rheum Dis"},{"issue":"1","key":"525_CR59","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1038\/nrg2484","volume":"10","author":"Z Wang","year":"2009","unstructured":"Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10(1):57\u201363. https:\/\/doi.org\/10.1038\/nrg2484","journal-title":"Nat Rev Genet"},{"key":"525_CR60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12864-018-4932-2","volume":"19","author":"L Wang","year":"2018","unstructured":"Wang L et al (2018a) RNA-seq assistant: machine learning based methods to identify more transcriptional regulated genes. BMC Genomics 19:1\u201313","journal-title":"BMC Genomics"},{"issue":"1","key":"525_CR61","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1186\/s12864-018-4932-2","volume":"19","author":"L Wang","year":"2018","unstructured":"Wang L et al (2018b) RNA-seq assistant: machine learning based methods to identify more transcriptional regulated genes. BMC Genomics 19(1):546. https:\/\/doi.org\/10.1186\/s12864-018-4932-2","journal-title":"BMC Genomics"},{"issue":"12","key":"525_CR62","doi-asserted-by":"publisher","first-page":"2036","DOI":"10.1002\/art.40576","volume":"70","author":"J Wang","year":"2018","unstructured":"Wang J et al (2018c) Association of abnormal elevations in IFIT 3 with overactive cyclic GMP-AMP synthase\/stimulator of interferon genes signaling in human systemic lupus erythematosus monocytes. Arthritis Rheumatol 70(12):2036\u20132045","journal-title":"Arthritis Rheumatol"},{"issue":"3","key":"525_CR63","doi-asserted-by":"publisher","first-page":"4024","DOI":"10.18632\/aging.202370","volume":"13","author":"C Wang","year":"2021","unstructured":"Wang C et al (2021) Identification of candidate genes encoding tumor-specific neoantigens in early- and late-stage colon adenocarcinoma. Aging (Albany NY) 13(3):4024\u20134044. https:\/\/doi.org\/10.18632\/aging.202370","journal-title":"Aging (Albany NY)"},{"issue":"10","key":"525_CR64","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1038\/nrrheum.2014.80","volume":"10","author":"HL Wright","year":"2014","unstructured":"Wright HL, Moots RJ, Edwards SW (2014) The multifactorial role of neutrophils in rheumatoid arthritis. Nat Rev Rheumatol 10(10):593\u2013601. https:\/\/doi.org\/10.1038\/nrrheum.2014.80","journal-title":"Nat Rev Rheumatol"},{"issue":"W1","key":"525_CR65","doi-asserted-by":"publisher","first-page":"W438","DOI":"10.1093\/nar\/gky439","volume":"46","author":"Q Wu","year":"2018","unstructured":"Wu Q et al (2018) COACH-D: improved protein\u2013ligand binding sites prediction with refined ligand-binding poses through molecular docking. Nucleic Acids Res 46(W1):W438\u2013W442","journal-title":"Nucleic Acids Res"},{"key":"525_CR66","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2022.831605","volume":"9","author":"Y Wu","year":"2022","unstructured":"Wu Y et al (2022) Integrated bioinformatics-based analysis of hub genes and the mechanism of immune infiltration associated with acute myocardial infarction. Front Cardiovasc Med 9:831605","journal-title":"Front Cardiovasc Med"},{"issue":"1","key":"525_CR67","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1186\/s13059-019-1689-0","volume":"20","author":"C Xu","year":"2019","unstructured":"Xu C, Jackson SA (2019) Machine learning and complex biological data. Genome Biol 20(1):76. https:\/\/doi.org\/10.1186\/s13059-019-1689-0","journal-title":"Genome Biol"},{"key":"525_CR68","doi-asserted-by":"publisher","first-page":"1084531","DOI":"10.3389\/fimmu.2023.1084531","volume":"14","author":"M Xu","year":"2023","unstructured":"Xu M et al (2023) Identification and validation of immune and oxidative stress-related diagnostic markers for diabetic nephropathy by WGCNA and machine learning. Front Immunol 14:1084531. https:\/\/doi.org\/10.3389\/fimmu.2023.1084531","journal-title":"Front Immunol"},{"issue":"9","key":"525_CR69","doi-asserted-by":"publisher","first-page":"1328","DOI":"10.1038\/cr.2012.111","volume":"22","author":"Z Yang","year":"2012","unstructured":"Yang Z et al (2012) Crystal structure of ISG54 reveals a novel RNA binding structure and potential functional mechanisms. Cell Res 22(9):1328\u20131338. https:\/\/doi.org\/10.1038\/cr.2012.111","journal-title":"Cell Res"},{"key":"525_CR70","doi-asserted-by":"crossref","unstructured":"Yao M, et al (2021) Exploration of the shared gene signatures and molecular mechanisms between systemic lupus erythematosus and pulmonary arterial hypertension: evidence from transcriptome data. Front Immunol","DOI":"10.3389\/fimmu.2021.658341"},{"issue":"5","key":"525_CR71","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1089\/omi.2011.0118","volume":"16","author":"G Yu","year":"2012","unstructured":"Yu G et al (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16(5):284\u2013287. https:\/\/doi.org\/10.1089\/omi.2011.0118","journal-title":"OMICS"},{"key":"525_CR72","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.3890","volume":"5","author":"G Zararsiz","year":"2017","unstructured":"Zararsiz G et al (2017) voomDDA: discovery of diagnostic biomarkers and classification of RNA-seq data. PeerJ 5:e3890. https:\/\/doi.org\/10.7717\/peerj.3890","journal-title":"PeerJ"},{"issue":"1","key":"525_CR73","first-page":"225","volume":"20","author":"Y-J Zhang","year":"2019","unstructured":"Zhang Y-J et al (2019) Integrated bioinformatic analysis of differentially expressed genes and signaling pathways in plaque psoriasis. Mol Med Rep 20(1):225\u2013235","journal-title":"Mol Med Rep"},{"issue":"3","key":"525_CR74","doi-asserted-by":"publisher","first-page":"lqaa078","DOI":"10.1093\/nargab\/lqaa078","volume":"2","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Parmigiani G, Johnson WE (2020) ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genom Bioinform 2(3):lqaa078","journal-title":"NAR Genom Bioinform"},{"key":"525_CR75","doi-asserted-by":"crossref","unstructured":"Zhang Z, Liu Z-P (2019) Identifying cancer biomarkers from high-throughput RNA sequencing data by machine learning. Springer.","DOI":"10.1007\/978-3-030-26969-2_49"}],"container-title":["Network Modeling Analysis in Health Informatics and Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13721-025-00525-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13721-025-00525-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13721-025-00525-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T17:03:11Z","timestamp":1746723791000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13721-025-00525-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,8]]},"references-count":75,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["525"],"URL":"https:\/\/doi.org\/10.1007\/s13721-025-00525-1","relation":{},"ISSN":["2192-6670"],"issn-type":[{"value":"2192-6670","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,8]]},"assertion":[{"value":"24 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 April 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"28"}}