{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T17:20:39Z","timestamp":1791566439540,"version":"4.3.4"},"reference-count":97,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T00:00:00Z","timestamp":1778112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:p>Systemic lupus erythematosus (SLE) is a clinically and biologically heterogeneous autoimmune disease in which patients with similar or disparate clinical phenotypes can exhibit distinct molecular drivers. This heterogeneity limits the utility of traditional, largely linear, analytic approaches and contributes to variations in diagnosis, prognosis, and therapeutic outcome. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offers a complementary framework for extracting nonlinear patterns from complex biomedical datasets and for translating high dimensional molecular measurements into actionable clinical signatures. Here, we review how supervised and unsupervised ML methods are being applied to SLE, with a focus on multi-omics integration across genomics, transcriptomics, proteomics, and metabolomics. In this review, we specifically focus on how these approaches can define molecular endotypes, explaining heterogeneity in disease course and therapeutic response. We summarize evidence that AI-driven endotyping can reproducibly separate patients into molecularly defined subgroups dominated by distinct immune signatures and facilitate biomarker discovery and improved risk modelling. We highlight the limitations of conventional clinical phenotyping and the need for AI-driven biologically grounded patient stratification. Importantly, we frame these advances within a systems biology perspective, in which AI-driven integration of multi-omics and clinical data enables a unified approach to disease diagnosis, molecular stratification, prognosis, and therapeutic response prediction in SLE. Despite these advances, most AI-derived predictors remain research-grade, and require prospective multi-center validation and explicit demonstration of clinical utility before routine deployment. If key barriers to clinical translation can be overcome, AI-based methods hold promise for achieving truly personalized approaches in the management of SLE.<\/jats:p>","DOI":"10.3389\/fimmu.2026.1804598","type":"journal-article","created":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T05:45:28Z","timestamp":1778132728000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Applications of artificial intelligence in systemic lupus erythematosus: integrating multi-omics data for precision medicine"],"prefix":"10.3389","volume":"17","author":[{"given":"Biswajit","family":"Biswas","sequence":"first","affiliation":[{"name":"Research and Development, ThinkBio.Ai","place":["Kochi, Kerala, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sana","family":"Munquad","sequence":"additional","affiliation":[{"name":"Research and Development, ThinkBio.Ai","place":["Kochi, Kerala, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kamalika","family":"Roy Choudhury","sequence":"additional","affiliation":[{"name":"Research and Development, ThinkBio.Ai","place":["Kochi, Kerala, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bobby J.","family":"Cherayil","sequence":"additional","affiliation":[{"name":"Research and Development, ThinkBio.Ai","place":["Kochi, Kerala, India"]},{"name":"Mucosal Immunology and Biology Research Center, Massachusetts General Hospital","place":["Boston, MA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amitabha","family":"Chaudhuri","sequence":"additional","affiliation":[{"name":"Research and Development, ThinkBio.Ai","place":["Kochi, Kerala, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,7]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"27848","DOI":"10.1038\/S41598-024-76093-7","article-title":"Machine learning for precision diagnostics of autoimmunity","volume":"14","author":"Kruta","year":"2024","journal-title":"Sci Rep"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/S2665-9913(23)00010-3","article-title":"Artificial intelligence and high-dimensional technologies in the theragnosis of systemic lupus erythematosus","volume":"5","author":"Yaung","year":"2023","journal-title":"Lancet Rheumatol"},{"key":"B3","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1038\/S41579-023-00984-1","article-title":"Machine learning for microbiologists","volume":"22","author":"Asnicar","year":"2024","journal-title":"Nat Rev Microbiol"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.3389\/FIMMU.2025.1580540","article-title":"Multi-omics integration identifies NK cell-mediated cytotoxicity as a therapeutic target in systemic lupus erythematosus","volume":"16","author":"Zhang","year":"2025","journal-title":"Front Immunol"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.3389\/FMED.2025.1686057","article-title":"Machine learning in lupus nephritis: bridging prediction models and clinical decision-making towards personalized nephrology","volume":"12","author":"Garcia-Ba\u00f1ol","year":"2025","journal-title":"Front Med (Lausanne)"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1136\/annrheumdis-2020-219069","article-title":"Lupus or not? SLE Risk Probability Index (SLERPI): a simple, clinician-friendly machine learning-based model to assist the diagnosis of systemic lupus erythematosus","volume":"80","author":"Adamichou","year":"2021","journal-title":"Ann Rheum Dis"},{"key":"B7","doi-asserted-by":"publisher","first-page":"e01093","DOI":"10.1002\/CTI2.1093","article-title":"Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus","volume":"8","author":"Figgett","year":"2019","journal-title":"Clin Transl Immunol"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000036243","article-title":"Screening of potential biomarkers of system lupus erythematosus based on WGCNA and machine learning algorithms","volume":"102","author":"Li","year":"2023","journal-title":"Med (United States)"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2025.1551450","article-title":"Identification of cellular senescence-related genes as biomarkers for lupus nephritis based on bioinformatics","volume":"16","author":"Chen","year":"2025","journal-title":"Front Genet"},{"key":"B10","doi-asserted-by":"publisher","first-page":"e071821","DOI":"10.1136\/BMJOPEN-2023-071821","article-title":"Deep learning model to predict lupus nephritis renal flare based on dynamic multivariable time-series data","volume":"14","author":"Huang","year":"2024","journal-title":"BMJ Open"},{"key":"B11","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1186\/S12882-022-02978-2","article-title":"Artificial neural network - an effective tool for predicting the lupus nephritis outcome","volume":"23","author":"Stojanowski","year":"2022","journal-title":"BMC Nephrol"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-023-06139-9","article-title":"Transfer learning enables predictions in network biology","volume":"618","author":"Theodoris","year":"2023","journal-title":"Nature"},{"key":"B13","doi-asserted-by":"publisher","first-page":"2025.06.11.659222","DOI":"10.1101\/2025.06.11.659222","article-title":"A large-scale foundation model for bulk transcriptomes","author":"Kang","year":"2025","journal-title":"bioRxiv"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-024-02201-0","article-title":"scGPT: toward building a foundation model for single-cell multi-omics using generative AI","volume":"21","author":"Cui","year":"2024","journal-title":"Nat Methods"},{"key":"B15","doi-asserted-by":"publisher","first-page":"3607","DOI":"10.1038\/s41467-025-58699-1","article-title":"scPRINT: pre-training on 50 million cells allows robust gene network predictions","volume":"16","author":"Kalfon","year":"2025","journal-title":"Nat Commun"},{"key":"B16","doi-asserted-by":"publisher","first-page":"9617","DOI":"10.1038\/S41598-019-45989-0","article-title":"Machine learning approaches to predict lupus disease activity from gene expression data","volume":"9","author":"Kegerreis","year":"2019","journal-title":"Sci Rep"},{"key":"B17","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1002\/GEPI.21608","article-title":"Bootstrap aggregating of alternating decision trees to detect sets of SNPs that associate with disease","volume":"36","author":"Guy","year":"2012","journal-title":"Genet Epidemiol"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2022.902793","article-title":"Random forests algorithm boosts genetic risk prediction of systemic lupus erythematosus","volume":"13","author":"Ma","year":"2022","journal-title":"Front Genet"},{"key":"B19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13040-023-00352-y","article-title":"Machine learning approaches to identify systemic lupus erythematosus in anti-nuclear antibody-positive patients using genomic data and electronic health records","volume":"17","author":"Chung","year":"2024","journal-title":"Biodata Min"},{"key":"B20","doi-asserted-by":"publisher","first-page":"keag035","DOI":"10.1093\/rheumatology\/keag035","article-title":"Identification of clinical phenotypes and disease trajectories in SLE using AI through a natural language processing framework","volume":"65","author":"Bosello","year":"2026","journal-title":"Rheumatol (Oxford)"},{"key":"B21","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1038\/s41390-024-03135-1","article-title":"DNA methylation of IFI44L as a potential blood biomarker for childhood-onset systemic lupus erythematosus","volume":"96","author":"Wang","year":"2024","journal-title":"Pediatr Res"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2024.1501783","article-title":"An updated review on abnormal epigenetic modifications in the pathogenesis of systemic lupus erythematosus","volume":"15","author":"Zhou","year":"2025","journal-title":"Front Immunol"},{"key":"B23","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1038\/s41525-024-00420-0","article-title":"Molecular subtypes explain lupus epigenomic heterogeneity unveiling new regulatory genetic risk variants","volume":"9","author":"Castellini-P\u00e9rez","year":"2024","journal-title":"NPJ Genom Med"},{"key":"B24","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1186\/s13148-024-01792-x","article-title":"Changes in DNA methylation are associated with systemic lupus erythematosus flare remission and clinical subtypes","volume":"16","author":"Horton","year":"2024","journal-title":"Clin Epigenet"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1016\/j.celrep.2022.111606","article-title":"Single-cell transcriptome profiling and chromatin accessibility reveal an exhausted regulatory CD4+ T cell subset in systemic lupus erythematosus","volume":"41","author":"Guo","year":"2022","journal-title":"Cell Rep"},{"key":"B26","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1186\/s13148-024-01754-3","article-title":"Comparison of cell type and disease subset chromatin modifications in SLE","volume":"16","author":"Beigel","year":"2024","journal-title":"Clin Epigenet"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2023.108042","article-title":"An interpretable machine learning pipeline based on transcriptomics predicts phenotypes of lupus patients","volume":"26","author":"Leventhal","year":"2023","journal-title":"iScience"},{"key":"B28","doi-asserted-by":"publisher","first-page":"7433","DOI":"10.1038\/S41598-022-10853-1","article-title":"Interpretable machine learning identifies paediatric Systemic Lupus Erythematosus subtypes based on gene expression data","volume":"12","author":"Yones","year":"2022","journal-title":"Sci Rep"},{"key":"B29","doi-asserted-by":"publisher","first-page":"15502","DOI":"10.1038\/s41598-019-51616-9","article-title":"Differential treatments based on drug-induced gene expression signatures and longitudinal systemic lupus erythematosus stratification","volume":"9","author":"Toro-Dom\u00ednguez","year":"2019","journal-title":"Sci Rep"},{"key":"B30","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1186\/S13073-023-01237-9","article-title":"Analysis of transcriptomic features reveals molecular endotypes of SLE with clinical implications","volume":"15","author":"Hubbard","year":"2023","journal-title":"Genome Med"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1093\/rheumatology\/keac625","article-title":"Deep stratification by transcriptome molecular characters for precision treatment of patients with systemic lupus erythematosus","volume":"62","author":"Qiao","year":"2023","journal-title":"Rheumatol (Oxford)"},{"key":"B32","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1186\/s12911-025-03056-x","article-title":"A deep learning model for predicting systemic lupus erythematosus-associated epitopes","volume":"25","author":"He","year":"2025","journal-title":"BMC Med Inform Decis Mak"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1136\/ard-2022-223808","article-title":"Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes","volume":"82","author":"Choi","year":"2023","journal-title":"Ann Rheum Dis"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2020.100291","article-title":"Adults with systemic lupus exhibit distinct molecular phenotypes in a cross-sectional study","volume":"20","author":"Guthridge","year":"2020","journal-title":"EClinicalMedicine"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1093\/RHEUMATOLOGY\/KEP058","article-title":"MALDI-TOF MS combined with magnetic beads for detecting serum protein biomarkers and establishment of boosting decision tree model for diagnosis of systemic lupus erythematosus","volume":"48","author":"Huang","year":"2009","journal-title":"Rheumatol (Oxford)"},{"key":"B36","doi-asserted-by":"publisher","first-page":"28765","DOI":"10.1038\/s41598-024-79978-9","article-title":"Machine learning identifies cytokine signatures of disease severity and autoantibody profiles in systemic lupus erythematosus - a pilot study","volume":"14","author":"Pattanaik","year":"2024","journal-title":"Sci Rep"},{"key":"B37","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1186\/s12014-023-09420-1","article-title":"Proteomic analysis identifies subgroups of patients with active systemic lupus erythematosus","volume":"20","author":"Su","year":"2023","journal-title":"Clin Proteomics"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.1093\/rheumatology\/keae158","article-title":"Machine learning-driven immunophenotypic stratification of mixed connective tissue disease, corroborating the clinical heterogeneity","volume":"64","author":"Izuka","year":"2025","journal-title":"Rheumatol (Oxford)"},{"key":"B39","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1186\/s13073-024-01350-3","article-title":"Machine learning integrative approaches to advance computational immunology","volume":"16","author":"Curion","year":"2024","journal-title":"Genome Med"},{"key":"B40","unstructured":"Machine Learning\u2013Defined Subtypes Of Systemic Lupus Erythematosus Identify Distinct Immunologic And Molecular Signatures - Acr Meeting Abstracts"},{"key":"B41","doi-asserted-by":"publisher","first-page":"149","DOI":"10.12688\/WELLCOMEOPENRES.16883.2","article-title":"Single-cell multi-omics analysis reveals IFN-driven alterations in T lymphocytes and natural killer cells in systemic lupus erythematosus","volume":"6","author":"Trzupek","year":"2022","journal-title":"Wellcome Open Res"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2022.969509","article-title":"Combined proteomics and single cell RNA-sequencing analysis to identify biomarkers of disease diagnosis and disease exacerbation for systemic lupus erythematosus","volume":"13","author":"Li","year":"2022","journal-title":"Front Immunol"},{"key":"B43","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2025.1567466","article-title":"Machine learning combined multi-omics analysis to explore key oxidative stress features in systemic lupus erythematosus","volume":"16","author":"Zhou","year":"2025","journal-title":"Front Immunol"},{"key":"B44","doi-asserted-by":"publisher","DOI":"10.1016\/j.insi.2025.100123","article-title":"Integrative machine learning of gene expression and DNA methylation for the accurate diagnosis of systemic lupus erythematosus and Sj\u00f6gren\u2019s syndrome","volume":"1","author":"Fareed","year":"2025","journal-title":"In Silico Res Biomedicine"},{"key":"B45","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-03141-0","article-title":"A foundation model for clinical-grade computational pathology and rare cancers detection","volume":"30","author":"Vorontsov","year":"2024","journal-title":"Nat Med"},{"key":"B46","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-02959-y","article-title":"Vision-language foundation model for echocardiogram interpretation","volume":"30","author":"Christensen","year":"2024","journal-title":"Nat Med"},{"key":"B47","doi-asserted-by":"publisher","first-page":"4679","DOI":"10.1038\/s41467-025-59926-5","article-title":"CellFM: a large-scale foundation model pre-trained on transcriptomics of 100 million human cells","volume":"16","author":"Zeng","year":"2025","journal-title":"Nat\u00a0Commun"},{"key":"B48","doi-asserted-by":"publisher","first-page":"2025.07.09.664020","DOI":"10.1101\/2025.07.09.664020","article-title":"ImmuneFM: Pre-training foundation model from cytometry data for immunology research","author":"Ding","year":"2025","journal-title":"bioRxiv"},{"key":"B49","doi-asserted-by":"publisher","DOI":"10.1136\/ard-2024-225664","article-title":"Disentangling the riddle of systemic lupus erythematosus with antiphospholipid syndrome: blood transcriptome analysis reveals a less-pronounced IFN-signature and distinct molecular profiles in venous versus arterial events","volume":"83","author":"Nikolopoulos","year":"2024","journal-title":"Ann Rheum Dis"},{"key":"B50","doi-asserted-by":"publisher","DOI":"10.1159\/000513566","article-title":"Machine learning for prediction and risk stratification of lupus nephritis renal flare","volume":"52","author":"Chen","year":"2021","journal-title":"Am J Nephrol"},{"key":"B51","doi-asserted-by":"publisher","first-page":"6578","DOI":"10.3390\/IJMS24076578","article-title":"Advances in the pathogenesis and treatment of systemic lupus erythematosus","volume":"24","author":"Accapezzato","year":"2023","journal-title":"Int J Mol Sci"},{"key":"B52","doi-asserted-by":"publisher","first-page":"154631","DOI":"10.1016\/j.cyto.2018.12.014","article-title":"Novel genetic associations with interferon in systemic lupus erythematosus identified by replication and fine-mapping of trait-stratified genome-wide screen","volume":"132","author":"Ghodke-Puranik","year":"2020","journal-title":"Cytokine"},{"key":"B53","doi-asserted-by":"publisher","first-page":"3902","DOI":"10.1038\/S41467-019-11845-Y","article-title":"A phenotypic and genomics approach in a multi-ethnic cohort to subtype systemic lupus erythematosus","volume":"10","author":"Lanata","year":"2019","journal-title":"Nat Commun"},{"key":"B54","doi-asserted-by":"publisher","DOI":"10.1146\/ANNUREV-MED-043021-032611","article-title":"Systemic lupus erythematosus: new diagnostic and therapeutic approaches","volume":"74","author":"Lazar","year":"2023","journal-title":"Annu Rev Med"},{"key":"B55","doi-asserted-by":"publisher","DOI":"10.3390\/BIOENGINEERING11010090","article-title":"Systemic lupus erythematosus: how machine learning can help distinguish between infections and flares","volume":"11","author":"Usategui","year":"2024","journal-title":"Bioengineering (Basel)"},{"key":"B56","doi-asserted-by":"publisher","DOI":"10.1136\/LUPUS-2023-001140","article-title":"Systemic lupus in the era of machine learning medicine","volume":"11","author":"Zhan","year":"2024","journal-title":"Lupus Sci Med"},{"key":"B57","doi-asserted-by":"publisher","DOI":"10.1016\/j.molmed.2020.09.009","article-title":"Leveraging heterogeneity in systemic lupus erythematosus for new therapies","volume":"27","author":"Allen","year":"2021","journal-title":"Trends Mol Med"},{"key":"B58","doi-asserted-by":"publisher","DOI":"10.7759\/CUREUS.40719","article-title":"The safety and efficacy of rituximab and belimumab in systemic lupus erythematosus: A systematic review","volume":"15","author":"Abid","year":"2023","journal-title":"Cureus"},{"key":"B59","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/S10067-017-3933-X","article-title":"An era of biological treatment in systemic lupus erythematosus","volume":"37","author":"He","year":"2018","journal-title":"Clin Rheumatol"},{"key":"B60","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2022.999704","article-title":"Biological drugs for systemic lupus erythematosus or active lupus nephritis and rates of infectious complications. Evidence from large clinical trials","volume":"13","author":"Steiger","year":"2022","journal-title":"Front Immunol"},{"key":"B61","doi-asserted-by":"publisher","DOI":"10.1007\/S40290-017-0178-6","article-title":"Defining biological subsets in systemic lupus erythematosus: progress toward personalized therapy","volume":"31","author":"Sinicato","year":"2017","journal-title":"Pharmaceut Med"},{"key":"B62","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2022.860726","article-title":"Molecular taxonomy of systemic lupus erythematosus through data-driven patient stratification: molecular endotypes and cluster-tailored drugs","volume":"13","author":"Garantziotis","year":"2022","journal-title":"Front Immunol"},{"key":"B63","doi-asserted-by":"publisher","DOI":"10.1093\/RHEUMATOLOGY\/KEAC695","article-title":"Genetics of SLE: does this explain susceptibility and severity across racial groups","volume":"62","author":"Demkova","year":"2023","journal-title":"Rheumatol (Oxford)"},{"key":"B64","doi-asserted-by":"publisher","DOI":"10.1016\/j.rdc.2014.05.003","article-title":"Environmental influences on systemic lupus erythematosus expression","volume":"40","author":"Kamen","year":"2014","journal-title":"Rheumatic Dis Clinics North America"},{"key":"B65","doi-asserted-by":"publisher","first-page":"bbac332","DOI":"10.1093\/BIB\/BBAC332","article-title":"Scoring personalized molecular portraits identify Systemic Lupus Erythematosus subtypes and predict individualized drug responses, symptomatology and disease progression","volume":"23","author":"Toro-Dom\u00ednguez","year":"2022","journal-title":"Brief Bioinform"},{"key":"B66","doi-asserted-by":"publisher","DOI":"10.1084\/JEM.20021553","article-title":"Interferon and granulopoiesis signatures in systemic lupus erythematosus blood","volume":"197","author":"Bennett","year":"2003","journal-title":"J Exp Med"},{"key":"B67","doi-asserted-by":"publisher","DOI":"10.1136\/LUPUS-2022-000861","article-title":"Molecular endotypes of type 1 and type 2 SLE","volume":"10","author":"Robl","year":"2023","journal-title":"Lupus Sci Med"},{"key":"B68","doi-asserted-by":"publisher","DOI":"10.1136\/ard-2024-226051","article-title":"Molecular basis for the disease-modifying effects of belimumab in systemic lupus erythematosus and molecular predictors of early response: blood transcriptome analysis implicates the innate immunity and DNA damage response pathways","volume":"84","author":"Moysidou","year":"2025","journal-title":"Ann Rheum Dis"},{"key":"B69","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.trsl.2021.07.006","article-title":"Cluster of highly expressed interferon-stimulated genes associate more with African ancestry than disease activity in patients with systemic lupus erythematosus. A systematic review of cross-sectional studies","volume":"238","author":"Siddiqi","year":"2021","journal-title":"Trans Res"},{"key":"B70","doi-asserted-by":"publisher","DOI":"10.1136\/ANNRHEUMDIS-2011-200937","article-title":"Belimumab in the treatment of systemic lupus erythematosus: high disease activity predictors of response","volume":"71","author":"Van Vollenhoven","year":"2012","journal-title":"Ann Rheum Dis"},{"key":"B71","doi-asserted-by":"publisher","DOI":"10.1002\/PSP4.12919","article-title":"Population model-based analysis of the memory B-cell response following belimumab therapy in the treatment of systemic lupus erythematosus","volume":"12","author":"Dimelow","year":"2023","journal-title":"CPT Pharmacometrics Syst Pharmacol"},{"key":"B72","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1007\/S10928-024-09907-W","article-title":"Longitudinal modeling of efficacy response in patients with lupus nephritis receiving belimumab","volume":"51","author":"Simeoni","year":"2024","journal-title":"J Pharmacokinet Pharmacodyn"},{"key":"B73","doi-asserted-by":"publisher","first-page":"177204","DOI":"10.1016\/j.ejphar.2024.177204","article-title":"Type I interferon-stimulated genes predict clinical response to belimumab in systemic lupus erythematosus","volume":"987","author":"Li","year":"2025","journal-title":"Eur J Pharmacol"},{"key":"B74","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1186\/S13075-020-02177-0","article-title":"The role of baseline BLyS levels and type 1 interferon-inducible gene signature status in determining belimumab response in systemic lupus erythematosus: a post hoc meta-analysis","volume":"22","author":"Wilkinson","year":"2020","journal-title":"Arthritis Res Ther"},{"key":"B75","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMOA1912196","article-title":"Trial of anifrolumab in active systemic lupus erythematosus","volume":"382","author":"Morand","year":"2020","journal-title":"N Engl J Med"},{"key":"B76","doi-asserted-by":"publisher","first-page":"929","DOI":"10.3390\/IJMS26030929","article-title":"Advances in targeted therapy for systemic lupus erythematosus: current treatments and novel approaches","volume":"26","author":"Saegusa","year":"2025","journal-title":"Int J Mol Sci"},{"key":"B77","doi-asserted-by":"publisher","DOI":"10.1093\/JAMIA\/OCY154","article-title":"Automated and flexible identification of complex disease: building a model for systemic lupus erythematosus using noisy labeling","volume":"26","author":"Murray","year":"2019","journal-title":"J Am Med Inform Assoc"},{"key":"B78","doi-asserted-by":"publisher","DOI":"10.1136\/annrheumdis-2021-222069","article-title":"Cross-species transcriptome analysis for early detection and specific therapeutic targeting of human lupus nephritis","volume":"81","author":"Frangou","year":"2022","journal-title":"Ann Rheum Dis"},{"key":"B79","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.semarthrit.2019.01.002","article-title":"Identifying lupus patients in electronic health records: Development and validation of machine learning algorithms and application of rule-based algorithms","volume":"49","author":"Jorge","year":"2019","journal-title":"Semin Arthritis Rheum"},{"key":"B80","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1186\/S12911-017-0518-1","article-title":"Word2Vec inversion and traditional text classifiers for phenotyping lupus","volume":"17","author":"Turner","year":"2017","journal-title":"BMC Med Inform Decis Mak"},{"key":"B81","doi-asserted-by":"publisher","DOI":"10.1177\/09612033221114805","article-title":"Exploration of machine learning methods to predict systemic lupus erythematosus hospitalizations","volume":"31","author":"Jorge","year":"2022","journal-title":"Lupus"},{"key":"B82","doi-asserted-by":"publisher","DOI":"10.1016\/j.kint.2021.11.014","article-title":"Prediction models of treatment response in lupus nephritis","volume":"101","author":"Ayoub","year":"2022","journal-title":"Kidney Int"},{"key":"B83","doi-asserted-by":"publisher","first-page":"e76557","DOI":"10.2196\/76557","article-title":"Multimodal integration in health care: Development with applications in disease management","volume":"27","author":"Hao","year":"2025","journal-title":"J Med Internet Res"},{"key":"B84","doi-asserted-by":"publisher","first-page":"4994","DOI":"10.1038\/S41467-025-60333-Z","article-title":"scMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links","volume":"16","author":"Wang","year":"2025","journal-title":"Nat Commun"},{"key":"B85","doi-asserted-by":"publisher","DOI":"10.1016\/j.xcrm.2025.102203","article-title":"A deep learning system for detecting systemic lupus erythematosus from retinal images","volume":"6","author":"Li","year":"2025","journal-title":"Cell Rep Med"},{"key":"B86","doi-asserted-by":"publisher","DOI":"10.1093\/RHEUMATOLOGY\/KEAD366","article-title":"Deep learning-based automatic scoring models for the disease activity of rheumatoid arthritis based on multimodal ultrasound images","volume":"63","author":"He","year":"2024","journal-title":"Rheumatol (Oxford)"},{"key":"B87","doi-asserted-by":"publisher","DOI":"10.1093\/RHEUMATOLOGY\/KEAE215","article-title":"Deep learning enables automatic detection of joint damage progression in rheumatoid arthritis-model development and external validation","volume":"64","author":"Venalainen","year":"2025","journal-title":"Rheumatol (Oxford)"},{"key":"B88","doi-asserted-by":"publisher","first-page":"11351","DOI":"10.1038\/S41598-023-38206-6","article-title":"Deep learning model for distinguishing Mayo endoscopic subscore 0 and 1 in patients with ulcerative colitis","volume":"13","author":"Kim","year":"2023","journal-title":"Sci Rep"},{"key":"B89","doi-asserted-by":"publisher","first-page":"17562848231170945","DOI":"10.1177\/17562848231170945","article-title":"Development and validation of a deep learning-based approach to predict the Mayo endoscopic score of ulcerative colitis","volume":"16","author":"Qi","year":"2023","journal-title":"Therap Adv Gastroenterol"},{"key":"B90","doi-asserted-by":"publisher","first-page":"103611","DOI":"10.1016\/j.nicl.2024.103611","article-title":"LST-AI: A deep learning ensemble for accurate MS lesion segmentation","volume":"42","author":"Wiltgen","year":"2024","journal-title":"NeuroImage Clin"},{"key":"B91","doi-asserted-by":"publisher","DOI":"10.1101\/2025.05.19.25327707","article-title":"FLAMeS: A robust deep learning model for automated multiple sclerosis lesion segmentation","author":"Dereskewicz","year":"2025","journal-title":"medRxiv"},{"key":"B92","doi-asserted-by":"publisher","first-page":"3759","DOI":"10.1038\/S41467-022-31449-3","article-title":"Deep learning to diagnose Hashimoto\u2019s thyroiditis from sonographic images","volume":"13","author":"Zhang","year":"2022","journal-title":"Nat Commun"},{"key":"B93","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2025.101679","article-title":"Challenges in AI-driven multi-omics data analysis for oncology: Addressing dimensionality, sparsity, transparency and ethical considerations","volume":"57","author":"Ouhmouk","year":"2025","journal-title":"Inform Med Unlocked"},{"key":"B94","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1136\/annrheumdis-2019-215694","article-title":"EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases","volume":"79","author":"Gossec","year":"2020","journal-title":"Ann Rheum Dis"},{"key":"B95","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/metabo10070286","article-title":"Find the needle in the haystack, then find it again: Replication and validation in the \u2018omics era","volume":"10","author":"Perng","year":"2020","journal-title":"Metabolites"},{"key":"B96","doi-asserted-by":"publisher","first-page":"648","DOI":"10.3390\/genes16060648","article-title":"A comprehensive review of deep learning applications with multi-omics data in cancer research","volume":"16","author":"Sartori","year":"2025","journal-title":"Genes (Basel)"},{"key":"B97","doi-asserted-by":"publisher","first-page":"2412775","DOI":"10.1002\/advs.202412775","article-title":"Integrative multi-omics and routine blood analysis using deep learning: Cost-effective early prediction of chronic disease risks","volume":"12","author":"Dong","year":"2025","journal-title":"Adv Sci (Weinh)"}],"container-title":["Frontiers in Immunology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fimmu.2026.1804598\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T05:45:35Z","timestamp":1778132735000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fimmu.2026.1804598\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,7]]},"references-count":97,"alternative-id":["10.3389\/fimmu.2026.1804598"],"URL":"https:\/\/doi.org\/10.3389\/fimmu.2026.1804598","relation":{},"ISSN":["1664-3224"],"issn-type":[{"value":"1664-3224","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,7]]},"article-number":"1804598"}}