{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T07:04:59Z","timestamp":1779951899473,"version":"3.53.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T00:00:00Z","timestamp":1758067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T00:00:00Z","timestamp":1758067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Guangdong-Hong Kong Technology Cooperation Funding Scheme","award":["GHX\/133\/20SZ"],"award-info":[{"award-number":["GHX\/133\/20SZ"]}]},{"name":"Young Collaborative Research grant","award":["C2004-23Y"],"award-info":[{"award-number":["C2004-23Y"]}]},{"DOI":"10.13039\/501100005847","name":"Health and Medical Research Fund","doi-asserted-by":"crossref","award":["11221026"],"award-info":[{"award-number":["11221026"]}],"id":[{"id":"10.13039\/501100005847","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001747","name":"Hong Kong Baptist University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001747","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Interdiscip Sci Comput Life Sci"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:sec>\n                    <jats:title>Abstract<\/jats:title>\n                    <jats:p>Traditional disease risk prediction models predominantly rely on statistical algorithms and often focus on genetic factors or a limited set of lifestyle factors to estimate the risk of disease onset. Recently, more comprehensive approaches have emerged that integrate genetic factors with additional lifestyle factors (e.g., alcohol intake) and physical features (e.g., body mass index, age) to increase predictive accuracy. Since the onset of complex diseases is often accompanied by the occurrence of comorbidities, incorporating medical history records is a critical yet underexplored avenue for improving risk prediction. In this study, we propose a novel framework, MIDRP (Multi-source Integration for Disease Risk Prediction), which incorporates genetic variants, lifestyle factors, physical attributes, and medical history records to achieve more robust and accurate predictions. At the heart of our approach lies a causal Transformer architecture, specifically designed to extract and interpret nuanced patterns from medical history records. In the experiments, we compared MIDRP with several baselines, including LDPred2, random forest, multilayer perception, logistic regression, AdaBoost, DiseaseCapsule, EIR, and Med-Bert, on three complex diseases Coronary Artery Disease, Type 2 Diabetes, and Breast Cancer using data from the UK Biobank. Our method achieved state-of-the-art performance, AUROC scores of 0.783, 0.841, and 0.784, respectively, demonstrating its potential in the field of complex disease risk prediction.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s12539-025-00749-9","type":"journal-article","created":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T20:28:36Z","timestamp":1758140916000},"page":"614-627","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Causal Transformer for Learning Embeddings from Structured Medical History Records and Multi-Source Data Integration for Complex Disease Risk Prediction"],"prefix":"10.1007","volume":"18","author":[{"given":"Zeming","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Debajyoti","family":"Chowdhury","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hip Fung","family":"Yip","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chonghao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,17]]},"reference":[{"issue":"4","key":"749_CR1","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1002\/gepi.21966","volume":"40","author":"F Dudbridge","year":"2016","unstructured":"Dudbridge F (2016) Polygenic epidemiology. Genet Epidemiol 40(4):268\u2013272. https:\/\/doi.org\/10.1002\/gepi.21966","journal-title":"Genet Epidemiol"},{"issue":"11","key":"749_CR2","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1038\/nrneurol.2013.203","volume":"9","author":"A Al-Chalabi","year":"2013","unstructured":"Al-Chalabi A, Hardiman O (2013) The epidemiology of als: a conspiracy of genes, environment and time. Nat Rev Neurol 9(11):617\u2013628. https:\/\/doi.org\/10.1038\/nrneurol.2013.203","journal-title":"Nat Rev Neurol"},{"key":"749_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2021.103795","author":"R Vasta","year":"2022","unstructured":"Vasta R, Chia R, Traynor BJ et al (2022) Unraveling the complex interplay between genes, environment, and climate in ALS. EBioMedicine 745:103795. https:\/\/doi.org\/10.1016\/j.ebiom.2021.103795","journal-title":"EBioMedicine"},{"issue":"1","key":"749_CR4","doi-asserted-by":"publisher","first-page":"bbac552","DOI":"10.1093\/bib\/bbac552","volume":"24","author":"C Wang","year":"2023","unstructured":"Wang C, Zhang J, Veldsman WP et al (2023) A comprehensive investigation of statistical and machine learning approaches for predicting complex human diseases on genomic variants. Brief Bioinform 24(1):bbac552. https:\/\/doi.org\/10.1093\/bib\/bbac552","journal-title":"Brief Bioinform"},{"key":"749_CR5","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.360300","author":"J Cramer","year":"2002","unstructured":"Cramer J (2002) The origins of logistic regression. Tinbergen Institute Discuss Papers. https:\/\/doi.org\/10.2139\/ssrn.360300","journal-title":"Tinbergen Inst Discuss Papers"},{"issue":"6","key":"749_CR6","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1002\/gepi.22050","volume":"41","author":"TSH Mak","year":"2017","unstructured":"Mak TSH, Porsch RM, Choi SW et al (2017) Polygenic scores via penalized regression on summary statistics. Genet Epidemiol 41(6):469\u2013480. https:\/\/doi.org\/10.1002\/gepi.22050","journal-title":"Genet Epidemiol"},{"issue":"2","key":"749_CR7","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1038\/s42256-022-00604-2","volume":"5","author":"X Luo","year":"2023","unstructured":"Luo X, Kang X, Sch\u00f6nhuth A (2023) Predicting the prevalence of complex genetic diseases from individual genotype profiles using capsule networks. Nature Mach Intell 5(2):114\u2013125. https:\/\/doi.org\/10.1038\/s42256-022-00604-2","journal-title":"Nature Mach Intell"},{"issue":"12","key":"749_CR8","doi-asserted-by":"publisher","first-page":"e67","DOI":"10.1093\/nar\/gkad373","volume":"51","author":"AI Sigurdsson","year":"2023","unstructured":"Sigurdsson AI, Louloudis I, Banasik K et al (2023) Deep integrative models for large-scale human genomics. Nucleic Acids Res 51(12):e67\u2013e67. https:\/\/doi.org\/10.1093\/nar\/gkad373","journal-title":"Nucleic Acids Res"},{"issue":"9","key":"749_CR9","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.1038\/ng.3379","volume":"47","author":"TL Lenz","year":"2015","unstructured":"Lenz TL, Deutsch AJ, Han B et al (2015) Widespread non-additive and interaction effects within hla loci modulate the risk of autoimmune diseases. Nat Genet 47(9):1085\u20131090. https:\/\/doi.org\/10.1038\/ng.3379","journal-title":"Nat Genet"},{"issue":"6224","key":"749_CR10","doi-asserted-by":"publisher","first-page":"1257601","DOI":"10.1126\/science.1257601","volume":"347","author":"J Menche","year":"2015","unstructured":"Menche J, Sharma A, Kitsak M et al (2015) Uncovering disease-disease relationships through the incomplete interactome. Science 347(6224):1257601. https:\/\/doi.org\/10.1126\/science.1257601","journal-title":"Science"},{"issue":"21","key":"749_CR11","doi-asserted-by":"publisher","first-page":"8685","DOI":"10.1073\/pnas.0701361104","volume":"104","author":"KI Goh","year":"2007","unstructured":"Goh KI, Cusick ME, Valle D et al (2007) The human disease network. Proc Natl Acad Sci 104(21):8685\u20138690. https:\/\/doi.org\/10.1073\/pnas.0701361104","journal-title":"Proc Natl Acad Sci"},{"issue":"12","key":"749_CR12","doi-asserted-by":"publisher","first-page":"1822","DOI":"10.1038\/s41591-019-0675-0","volume":"25","author":"D Furman","year":"2019","unstructured":"Furman D, Campisi J, Verdin E et al (2019) Chronic inflammation in the etiology of disease across the life span. Nat Med 25(12):1822\u20131832. https:\/\/doi.org\/10.1038\/s41591-019-0675-0","journal-title":"Nat Med"},{"issue":"18","key":"749_CR13","doi-asserted-by":"publisher","first-page":"2015","DOI":"10.1016\/j.jacc.2017.12.068","volume":"71","author":"MV Madhavan","year":"2018","unstructured":"Madhavan MV, Gersh BJ, Alexander KP et al (2018) Coronary artery disease in patients? 80 years of age. J Am Coll Cardiol 71(18):2015\u20132040. https:\/\/doi.org\/10.1016\/j.jacc.2017.12.068","journal-title":"J Am Coll Cardiol"},{"issue":"4","key":"749_CR14","doi-asserted-by":"publisher","first-page":"316","DOI":"10.2337\/cd19-0001","volume":"37","author":"RH Eckel","year":"2019","unstructured":"Eckel RH, Farooki A, Henry RR et al (2019) Cardiovascular outcome trials in type 2 diabetes: What do they mean for clinical practice? Clinical Diabetes 37(4):316\u2013337. https:\/\/doi.org\/10.2337\/cd19-0001","journal-title":"Clinical Diabetes"},{"issue":"1","key":"749_CR15","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1038\/s41746-021-00455-y","volume":"4","author":"L Rasmy","year":"2021","unstructured":"Rasmy L, Xiang Y, Xie Z et al (2021) Med-bert: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. NPJ Dig Med 4(1):86. https:\/\/doi.org\/10.1038\/s41746-021-00455-y","journal-title":"NPJ Dig Med"},{"key":"749_CR16","doi-asserted-by":"publisher","unstructured":"Devlin J (2018) Bert: pre-training of deep bidirectional transformers for language understanding. ArXiv. https:\/\/doi.org\/10.48550\/arXiv.1810.04805","DOI":"10.48550\/arXiv.1810.04805"},{"key":"749_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.physd.2019.132306","volume":"404","author":"A Sherstinsky","year":"2020","unstructured":"Sherstinsky A (2020) Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D 404:132306. https:\/\/doi.org\/10.1016\/j.physd.2019.132306","journal-title":"Physica D"},{"key":"749_CR18","doi-asserted-by":"publisher","unstructured":"Chung J, Gulcehre C, Cho K et\u00a0al (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. https:\/\/doi.org\/10.48550\/arXiv.1412.3555","DOI":"10.48550\/arXiv.1412.3555"},{"key":"749_CR19","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.34408","volume":"7","author":"G Hemani","year":"2018","unstructured":"Hemani G, Zheng J, Elsworth B et al (2018) The MR-Base platform supports systematic causal inference across the human phenome. eLife 7:e34408. https:\/\/doi.org\/10.7554\/eLife.34408","journal-title":"Elife"},{"issue":"4","key":"749_CR20","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1136\/ebmental-2019-300117","volume":"22","author":"S Balduzzi","year":"2019","unstructured":"Balduzzi S, R\u00fccker G, Schwarzer G (2019) How to perform a meta-analysis with R: a practical tutorial. BMJ Ment Health 22(4):153\u2013160. https:\/\/doi.org\/10.1136\/ebmental-2019-300117","journal-title":"BMJ Ment Health"},{"key":"749_CR21","doi-asserted-by":"publisher","unstructured":"Xu Y, Wang C, Li Z et\u00a0al (2022) A machine learning model for disease risk prediction by integrating genetic and non-genetic factors. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp 868\u2013871. https:\/\/doi.org\/10.1109\/BIBM55620.2022.9994925","DOI":"10.1109\/BIBM55620.2022.9994925"},{"key":"749_CR22","unstructured":"Ganchev K, Graca J, Gillenwater J et\u00a0al (2009) Posterior regularization for structured latent variable models. JMLR 11:2001\u20132049, p 912. http:\/\/jmlr.org\/papers\/v11\/ganchev10a.html"},{"key":"749_CR23","doi-asserted-by":"publisher","unstructured":"Ma F, Gao J, Suo Q, et\u00a0al (2018) Risk prediction on electronic health records with prior medical knowledge. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Association for Computing Machinery, pp 1910\u20131919, https:\/\/doi.org\/10.1145\/3219819.3220020","DOI":"10.1145\/3219819.3220020"},{"issue":"3","key":"749_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pmed.1001779","volume":"12","author":"C Sudlow","year":"2015","unstructured":"Sudlow C, Gallacher J, Allen N et al (2015) UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 12(3):1\u201310. https:\/\/doi.org\/10.1371\/journal.pmed.1001779","journal-title":"PLoS Med"},{"issue":"3","key":"749_CR25","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1086\/519795","volume":"81","author":"S Purcell","year":"2007","unstructured":"Purcell S, Neale B, Todd-Brown K et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81(3):559\u2013575. https:\/\/doi.org\/10.1086\/519795","journal-title":"Am J Hum Genet"},{"issue":"1","key":"749_CR26","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1186\/s13059-020-02248-0","volume":"22","author":"MS Lyon","year":"2021","unstructured":"Lyon MS, Andrews SJ, Elsworth B et al (2021) The variant call format provides efficient and robust storage of GWAS summary statistics. Genome Biol 22(1):32. https:\/\/doi.org\/10.1186\/s13059-020-02248-0","journal-title":"Genome Biol"},{"key":"749_CR27","doi-asserted-by":"publisher","unstructured":"Elsworth B, Lyon M, Alexander T et\u00a0al (2020) The MRC IEU OpenGWAS data infrastructure. bioRxiv. https:\/\/doi.org\/10.1101\/2020.08.10.244293","DOI":"10.1101\/2020.08.10.244293"},{"key":"749_CR28","doi-asserted-by":"publisher","unstructured":"Hemani G, Zheng J, Elsworth B et\u00a0al (2018) The MR-base platform supports systematic causal inference across the human phenome. eLife 7:e34408. https:\/\/doi.org\/10.7554\/eLife.34408","DOI":"10.7554\/eLife.34408"},{"key":"749_CR29","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2022.756901","author":"F Yang","year":"2022","unstructured":"Yang F, Hu T, Chen S et al (2022) Low intelligence predicts higher risks of coronary artery disease and myocardial infarction: evidence from mendelian randomization study. Front Genet. https:\/\/doi.org\/10.3389\/fgene.2022.756901","journal-title":"Front Genet"},{"issue":"20","key":"749_CR30","doi-asserted-by":"publisher","first-page":"3207","DOI":"10.1093\/bioinformatics\/btw373","volume":"32","author":"JR Staley","year":"2016","unstructured":"Staley JR, Blackshaw J, Kamat MA et al (2016) PhenoScanner: a database of human genotype-phenotype associations. Bioinformatics 32(20):3207\u20133209. https:\/\/doi.org\/10.1093\/bioinformatics\/btw373","journal-title":"Bioinformatics"},{"key":"749_CR31","doi-asserted-by":"publisher","unstructured":"The 1000 Genomes Project Consortium et al (2015) A global reference for human genetic variation. Nature 526(7571):68. https:\/\/doi.org\/10.1038\/nature15393","DOI":"10.1038\/nature15393"},{"issue":"6","key":"749_CR32","doi-asserted-by":"publisher","first-page":"1734","DOI":"10.1093\/ije\/dyx034","volume":"46","author":"OO Yavorska","year":"2017","unstructured":"Yavorska OO, Burgess S (2017) MendelianRandomization: an R package for performing mendelian randomization analyses using summarized data. Int J Epidemiol 46(6):1734\u20131739. https:\/\/doi.org\/10.1093\/ije\/dyx034","journal-title":"Int J Epidemiol"},{"key":"749_CR33","unstructured":"Vaswani A, Shazeer N, Parmar N et\u00a0al (2017) Attention is all you need. In: Guyon I, Luxburg UV, Bengio S, et\u00a0al (eds) Advances in Neural Information Processing Systems, pp 1\u201311. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf"},{"key":"749_CR34","doi-asserted-by":"publisher","unstructured":"Zuo S, Jiang H, Li Z et\u00a0al (2020) Transformer Hawkes process. In: International Conference on Machine Learning, PMLR. Arxiv, pp 11692\u201311702. https:\/\/doi.org\/10.48550\/arXiv.2002.09291","DOI":"10.48550\/arXiv.2002.09291"},{"key":"749_CR35","doi-asserted-by":"crossref","unstructured":"Vig J (2019) A multiscale visualization of attention in the transformer model. arXiv. https:\/\/api.semanticscholar.org\/CorpusID:189762556","DOI":"10.18653\/v1\/P19-3007"},{"issue":"4","key":"749_CR36","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1016\/j.ajhg.2015.09.001","volume":"97","author":"BJ Vilhj\u00e1lmsson","year":"2015","unstructured":"Vilhj\u00e1lmsson BJ, Yang J, Finucane HK et al (2015) Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am J Hum Genet 97(4):576\u2013592. https:\/\/doi.org\/10.1016\/j.ajhg.2015.09.001","journal-title":"Am J Hum Genet"},{"issue":"22\u201323","key":"749_CR37","doi-asserted-by":"publisher","first-page":"5424","DOI":"10.1093\/bioinformatics\/btaa1029","volume":"36","author":"F Priv\u00e9","year":"2020","unstructured":"Priv\u00e9 F, Arbel J, Vilhj\u00e1lmsson BJ (2020) LDpred2: better, faster, stronger. Bioinformatics 36(22\u201323):5424\u20135431. https:\/\/doi.org\/10.1093\/bioinformatics\/btaa1029","journal-title":"Bioinformatics"},{"key":"749_CR38","doi-asserted-by":"publisher","first-page":"2825","DOI":"10.5555\/1953048.2078195","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825\u20132830. https:\/\/doi.org\/10.5555\/1953048.2078195","journal-title":"J Mach Learn Res"},{"issue":"1","key":"749_CR39","doi-asserted-by":"publisher","first-page":"1776","DOI":"10.1038\/s41467-019-09718-5","volume":"10","author":"T Ge","year":"2019","unstructured":"Ge T, Chen CY, Ni Y et al (2019) Polygenic prediction via bayesian regression and continuous shrinkage priors. Nat Commun 10(1):1776. https:\/\/doi.org\/10.1038\/s41467-019-09718-5","journal-title":"Nat Commun"},{"issue":"3","key":"749_CR40","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1177\/1759720X10365301","volume":"2","author":"A Al Husain","year":"2010","unstructured":"Al Husain A, Bruce IN (2010) Risk factors for coronary heart disease in connective tissue diseases. Ther Adv Musculoskeletal Dis 2(3):145\u2013153. https:\/\/doi.org\/10.1177\/1759720X10365301","journal-title":"Ther Adv Musculoskeletal Dis"},{"issue":"3","key":"749_CR41","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1016\/j.ijcard.2013.12.079","volume":"171","author":"P Ungprasert","year":"2014","unstructured":"Ungprasert P, Wannarong T, Panichsillapakit T et al (2014) Cardiac involvement in mixed connective tissue disease: a systematic review. Int J Cardiol 171(3):326\u2013330. https:\/\/doi.org\/10.1016\/j.ijcard.2013.12.079","journal-title":"Int J Cardiol"},{"key":"749_CR42","doi-asserted-by":"publisher","unstructured":"Barbieri J, Fontela PC, Winkelmann ER et al (2015) Anemia in patients with type 2 diabetes mellitus. Anemia 2015. https:\/\/doi.org\/10.1155\/2015\/354737","DOI":"10.1155\/2015\/354737"},{"issue":"6","key":"749_CR43","doi-asserted-by":"publisher","first-page":"1837","DOI":"10.1016\/j.dsx.2020.09.020","volume":"14","author":"M Praveen","year":"2020","unstructured":"Praveen M, Jain N, Raizada N et al (2020) Anaemia in patients with type 2 diabetes mellitus without nephropathy is related to iron deficiency. Diabetes Metab Syndrome Clin Res Rev 14(6):1837\u20131840. https:\/\/doi.org\/10.1016\/j.dsx.2020.09.020","journal-title":"Diabetes Metab Syndrome Clin Res Rev"},{"key":"749_CR44","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.nutos.2022.08.006","volume":"45","author":"K Mokgalaboni","year":"2022","unstructured":"Mokgalaboni K, Phoswa W (2022) Cross-link between type 2 diabetes mellitus and iron deficiency anemia. a mini-review. Clin Nutr Open Sci 45:57\u201371. https:\/\/doi.org\/10.1016\/j.nutos.2022.08.006","journal-title":"Clin Nutr Open Sci"},{"issue":"6509","key":"749_CR45","doi-asserted-by":"publisher","first-page":"1597","DOI":"10.1136\/bmj.291.6509.1597","volume":"291","author":"B Goudie","year":"1985","unstructured":"Goudie B, Burt A, Boyle P et al (1985) Breast cancer in women with primary biliary cirrhosis. Br Med J (Clin Res Ed) 291(6509):1597. https:\/\/doi.org\/10.1136\/bmj.291.6509.1597","journal-title":"Br Med J (Clin Res Ed)"},{"issue":"7","key":"749_CR46","doi-asserted-by":"publisher","first-page":"1030","DOI":"10.1016\/j.dld.2018.12.024","volume":"51","author":"MS Kwak","year":"2019","unstructured":"Kwak MS, Yim JY, Yi A et al (2019) Nonalcoholic fatty liver disease is associated with breast cancer in nonobese women. Dig Liver Dis 51(7):1030\u20131035. https:\/\/doi.org\/10.1016\/j.dld.2018.12.024","journal-title":"Dig Liver Dis"}],"container-title":["Interdisciplinary Sciences: Computational Life Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12539-025-00749-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12539-025-00749-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12539-025-00749-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T06:33:03Z","timestamp":1779949983000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12539-025-00749-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,17]]},"references-count":46,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["749"],"URL":"https:\/\/doi.org\/10.1007\/s12539-025-00749-9","relation":{},"ISSN":["1913-2751","1867-1462"],"issn-type":[{"value":"1913-2751","type":"print"},{"value":"1867-1462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,17]]},"assertion":[{"value":"6 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 July 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 July 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 September 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}