{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T09:48:42Z","timestamp":1767174522372,"version":"build-2238731810"},"reference-count":81,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T00:00:00Z","timestamp":1759104000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T00:00:00Z","timestamp":1759104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"R&D Program of Guangzhou National Laboratory","award":["ZL-SRPG22-019, DM"],"award-info":[{"award-number":["ZL-SRPG22-019, DM"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Genomics"],"DOI":"10.1186\/s12864-025-12093-9","type":"journal-article","created":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T19:23:51Z","timestamp":1759173831000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MWENA: a novel sample re-weighting-based algorithm for disease classification and data interpretation using extracellular vesicles omics data"],"prefix":"10.1186","volume":"26","author":[{"given":"Shuilin","family":"Liao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haonan","family":"Long","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shoujiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Le","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shanghui","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nan","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,29]]},"reference":[{"issue":"1","key":"12093_CR1","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1186\/s12943-022-01509-9","volume":"21","author":"D Yu","year":"2022","unstructured":"Yu D, Li Y, Wang M, Gu J, Xu W, Cai H, et al. Exosomes as a new frontier of cancer liquid biopsy. Mol Cancer. 2022;21(1):56.","journal-title":"Mol Cancer"},{"issue":"1","key":"12093_CR2","doi-asserted-by":"publisher","DOI":"10.1002\/sstr.202100096","volume":"3","author":"Y Xue","year":"2022","unstructured":"Xue Y, Feng X, Fan X, Zhu G, McLaughlan J, Zhang W, et al. Extracellular vesicles for the diagnosis of cancers. Small Struct. 2022;3(1):2100096.","journal-title":"Small Struct"},{"key":"12093_CR3","doi-asserted-by":"publisher","first-page":"792046","DOI":"10.3389\/fimmu.2022.792046","volume":"13","author":"C Hu","year":"2022","unstructured":"Hu C, Jiang W, Lv M, Fan S, Lu Y, Wu Q, et al. Potentiality of exosomal proteins as novel cancer biomarkers for liquid biopsy. Front Immunol. 2022;13:792046.","journal-title":"Front Immunol"},{"issue":"6478","key":"12093_CR4","doi-asserted-by":"publisher","DOI":"10.1126\/science.aau6977","volume":"367","author":"R Kalluri","year":"2020","unstructured":"Kalluri R, LeBleu VS. The biology, function, and biomedical applications of exosomes. Science. 2020;367(6478):eaau6977.","journal-title":"Science"},{"issue":"6","key":"12093_CR5","doi-asserted-by":"publisher","first-page":"798","DOI":"10.1373\/clinchem.2018.301291","volume":"65","author":"Y Li","year":"2019","unstructured":"Li Y, Zhao J, Yu S, Wang Z, He X, Su Y, et al. Extracellular vesicles long RNA sequencing reveals abundant mRNA, circRNA, and lncRNA in human blood as potential biomarkers for cancer diagnosis. Clin Chem. 2019;65(6):798\u2013808.","journal-title":"Clin Chem"},{"issue":"1","key":"12093_CR6","doi-asserted-by":"publisher","DOI":"10.1080\/20013078.2018.1535750","volume":"7","author":"C Th\u00e9ry","year":"2018","unstructured":"Th\u00e9ry C, Witwer KW, Aikawa E, Alcaraz MJ, Anderson JD, Andriantsitohaina R, et al. Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. J Extracell Vesicles. 2018;7(1):1535750.","journal-title":"J Extracell Vesicles"},{"issue":"4","key":"12093_CR7","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1038\/nrm.2017.125","volume":"19","author":"G Van Niel","year":"2018","unstructured":"Van Niel G, d\u2019Angelo G, Raposo G. Shedding light on the cell biology of extracellular vesicles. Nat Rev Mol Cell Biol. 2018;19(4):213\u201328.","journal-title":"Nat Rev Mol Cell Biol"},{"issue":"10","key":"12093_CR8","doi-asserted-by":"publisher","first-page":"958","DOI":"10.1056\/NEJMra1704286","volume":"379","author":"R Shah","year":"2018","unstructured":"Shah R, Patel T, Freedman JE. Circulating extracellular vesicles in human disease. N Engl J Med. 2018;379(10):958\u201366.","journal-title":"N Engl J Med"},{"issue":"9","key":"12093_CR9","doi-asserted-by":"publisher","first-page":"1744","DOI":"10.1016\/j.cmet.2021.08.006","volume":"33","author":"R Isaac","year":"2021","unstructured":"Isaac R, Reis FCG, Ying W, Olefsky JM. Exosomes as mediators of intercellular crosstalk in metabolism. Cell Metab. 2021;33(9):1744\u201362.","journal-title":"Cell Metab"},{"issue":"D1","key":"12093_CR10","doi-asserted-by":"publisher","first-page":"D106","DOI":"10.1093\/nar\/gkx891","volume":"46","author":"S Li","year":"2018","unstructured":"Li S, Li Y, Chen B, Zhao J, Yu S, Tang Y, et al. exoRBase: a database of circRNA, lncRNA and mRNA in human blood exosomes. Nucleic Acids Res. 2018;46(D1):D106\u201312.","journal-title":"Nucleic Acids Res"},{"key":"12093_CR11","doi-asserted-by":"publisher","first-page":"434068","DOI":"10.3389\/fimmu.2019.00202","volume":"10","author":"A Turchinovich","year":"2019","unstructured":"Turchinovich A, Drapkina O, Tonevitsky A. Transcriptome of extracellular vesicles: state-of-the-art. Front Immunol. 2019;10:434068.","journal-title":"Front Immunol"},{"issue":"4","key":"12093_CR12","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1002\/mas.21420","volume":"34","author":"DS Choi","year":"2015","unstructured":"Choi DS, Kim DK, Kim YK, Gho YS. Proteomics of extracellular vesicles: exosomes and ectosomes. Mass Spectrom Rev. 2015;34(4):474\u201390.","journal-title":"Mass Spectrom Rev"},{"issue":"1","key":"12093_CR13","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1002\/mas.21457","volume":"35","author":"G Pocsfalvi","year":"2016","unstructured":"Pocsfalvi G, Stanly C, Vilasi A, Fiume I, Capasso G, Turi\u00e1k L, et al. Mass spectrometry of extracellular vesicles. Mass Spectrom Rev. 2016;35(1):3\u201321.","journal-title":"Mass Spectrom Rev"},{"issue":"2","key":"12093_CR14","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.biochi.2006.10.014","volume":"89","author":"C Subra","year":"2007","unstructured":"Subra C, Laulagnier K, Perret B, Record M. Exosome lipidomics unravels lipid sorting at the level of multivesicular bodies. Biochimie. 2007;89(2):205\u201312.","journal-title":"Biochimie"},{"issue":"16","key":"12093_CR15","doi-asserted-by":"publisher","first-page":"1930","DOI":"10.1039\/D1AY00060H","volume":"13","author":"F Guan","year":"2021","unstructured":"Guan F, Xiang X, Xie Y, Li H, Zhang W, Shu Y, et al. Simultaneous metabolomics and proteomics analysis of plasma-derived extracellular vesicles. Anal Methods. 2021;13(16):1930\u20138.","journal-title":"Anal Methods"},{"key":"12093_CR16","doi-asserted-by":"crossref","unstructured":"Chen Z, Huo D, Yang X, Cai Z, Xiao H, Wang T. Methods for the Isolation of Extracellular Vesicles. In: Wang Q, Zheng L, editors.\u00a0Extracellular Vesicles: From Bench to Bedside. Singapore: Springer; 2024. p. 69\u201382.","DOI":"10.1007\/978-981-99-8365-0_6"},{"key":"12093_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.bios.2022.114487","volume":"214","author":"L Zhao","year":"2022","unstructured":"Zhao L, Wang H, Fu J, Wu X, Liang XY, Liu XY, et al. Microfluidic-based exosome isolation and highly sensitive aptamer exosome membrane protein detection for lung cancer diagnosis. Biosens Bioelectron. 2022;214:114487.","journal-title":"Biosens Bioelectron"},{"issue":"6","key":"12093_CR18","doi-asserted-by":"publisher","first-page":"1217","DOI":"10.1109\/TPAMI.2015.2478471","volume":"38","author":"N Armanfard","year":"2015","unstructured":"Armanfard N, Reilly JP, Komeili M. Local feature selection for data classification. IEEE Trans Pattern Anal Mach Intell. 2015;38(6):1217\u201327.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"9","key":"12093_CR19","doi-asserted-by":"publisher","first-page":"6283","DOI":"10.1002\/int.22844","volume":"37","author":"H Huang","year":"2022","unstructured":"Huang H, Wu N, Liang Y, Peng X, Shu J. Slnl: a novel method for gene selection and phenotype classification. Int J Intell Syst. 2022;37(9):6283\u2013304.","journal-title":"Int J Intell Syst"},{"issue":"19","key":"12093_CR20","doi-asserted-by":"publisher","first-page":"2348","DOI":"10.1093\/bioinformatics\/btl386","volume":"22","author":"GC Cawley","year":"2006","unstructured":"Cawley GC, Talbot NL. Gene selection in cancer classification using sparse logistic regression with Bayesian regularization. Bioinformatics. 2006;22(19):2348\u201355.","journal-title":"Bioinformatics"},{"issue":"1","key":"12093_CR21","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc B Stat Methodol. 1996;58(1):267\u201388.","journal-title":"J R Stat Soc B Stat Methodol"},{"issue":"1","key":"12093_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v033.i01","volume":"33","author":"J Friedman","year":"2010","unstructured":"Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33(1):1.","journal-title":"J Stat Softw"},{"issue":"2","key":"12093_CR23","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Ser B Stat Methodol. 2005;67(2):301\u201320.","journal-title":"J R Stat Soc Ser B Stat Methodol"},{"key":"12093_CR24","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.compbiomed.2015.10.008","volume":"67","author":"ZY Algamal","year":"2015","unstructured":"Algamal ZY, Lee MH. Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification. Comput Biol Med. 2015;67:136\u201345.","journal-title":"Comput Biol Med"},{"issue":"1","key":"12093_CR25","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1002\/wics.14","volume":"1","author":"GC McDonald","year":"2009","unstructured":"McDonald GC. Ridge regression. Wiley Interdiscip Rev Comput Stat. 2009;1(1):93\u2013100.","journal-title":"Wiley Interdiscip Rev Comput Stat"},{"key":"12093_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-14-198","volume":"14","author":"Y Liang","year":"2013","unstructured":"Liang Y, Liu C, Luan XZ, Leung KS, Chan TM, Xu ZB, et al. Sparse logistic regression with a L 1\/2 penalty for gene selection in cancer classification. BMC Bioinformatics. 2013;14:1\u201312.","journal-title":"BMC Bioinformatics"},{"key":"12093_CR27","first-page":"19290","volume":"33","author":"Y Yang","year":"2020","unstructured":"Yang Y, Xu Z. Rethinking the value of labels for improving class-imbalanced learning. Adv Neural Inf Process Syst. 2020;33:19290\u2013301.","journal-title":"Adv Neural Inf Process Syst"},{"issue":"6","key":"12093_CR28","doi-asserted-by":"publisher","first-page":"2797","DOI":"10.1002\/int.22402","volume":"36","author":"G Yu","year":"2021","unstructured":"Yu G, Zeng J, Wang J, Zhang H, Zhang X, Guo M. Imbalance deep multi-instance learning for predicting isoform-isoform interactions. Int J Intell Syst. 2021;36(6):2797\u2013824.","journal-title":"Int J Intell Syst"},{"key":"12093_CR29","unstructured":"Shu J, Xie Q, Yi L, Zhao Q, Zhou S, Xu Z, et\u00a0al. Meta-weight-net: Learning an explicit mapping for sample weighting. Adv Neural Inf Process Syst. 2019;32:1917\u201328."},{"issue":"1","key":"12093_CR30","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund Y, Schapire RE. A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci. 1997;55(1):119\u201339.","journal-title":"J Comput Syst Sci"},{"issue":"12","key":"12093_CR31","doi-asserted-by":"publisher","first-page":"3358","DOI":"10.1016\/j.patcog.2007.04.009","volume":"40","author":"Y Sun","year":"2007","unstructured":"Sun Y, Kamel MS, Wong AK, Wang Y. Cost-sensitive boosting for classification of imbalanced data. Pattern Recognit. 2007;40(12):3358\u201378.","journal-title":"Pattern Recognit"},{"key":"12093_CR32","doi-asserted-by":"crossref","unstructured":"Lin TY, Goyal P, Girshick R, He K, Doll\u00e1r P. Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. IEEE; 2017. p. 2999\u20133007.","DOI":"10.1109\/ICCV.2017.324"},{"key":"12093_CR33","unstructured":"Kumar M, Packer B, Koller D. Self-paced learning for latent variable models. Adv Neural Inf Process Syst. 2010;23:1189\u201397."},{"key":"12093_CR34","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1023\/A:1023709501986","volume":"54","author":"F De La Torre","year":"2003","unstructured":"De La Torre F, Black MJ. A framework for robust subspace learning. Int J Comput Vis. 2003;54:117\u201342.","journal-title":"Int J Comput Vis"},{"key":"12093_CR35","unstructured":"Zhang Z, Sabuncu M. Generalized cross entropy loss for training deep neural networks with noisy labels. Adv Neural Inf Process Syst. 2018;31:1\u201311."},{"key":"12093_CR36","unstructured":"Wang L, Cai Q, Yang Z, Wang Z. On the global optimality of model-agnostic meta-learning. International conference on machine learning (ICML). PMLR. 2020:9837\u201346."},{"issue":"1","key":"12093_CR37","doi-asserted-by":"publisher","first-page":"6350","DOI":"10.1038\/s41467-020-20167-3","volume":"11","author":"YL Qiu","year":"2020","unstructured":"Qiu YL, Zheng H, Devos A, Selby H, Gevaert O. A meta-learning approach for genomic survival analysis. Nat Commun. 2020;11(1):6350.","journal-title":"Nat Commun"},{"key":"12093_CR38","doi-asserted-by":"crossref","unstructured":"Lv Q, Chen G, Yang Z, Zhong W, Chen CYC. Meta learning with graph attention networks for low-data drug discovery. IEEE Trans Neural Netw Learn Syst. 2023;35(8):11218\u201330.","DOI":"10.1109\/TNNLS.2023.3250324"},{"issue":"1","key":"12093_CR39","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1021\/acs.jproteome.9b00496","volume":"19","author":"AD Shah","year":"2019","unstructured":"Shah AD, Goode RJ, Huang C, Powell DR, Schittenhelm RB. Lfq-analyst: an easy-to-use interactive web platform to analyze and visualize label-free proteomics data preprocessed with MaxQuant. J Proteome Res. 2019;19(1):204\u201311.","journal-title":"J Proteome Res"},{"issue":"7","key":"12093_CR40","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1002\/sim.4780090710","volume":"9","author":"Y Hochberg","year":"1990","unstructured":"Hochberg Y, Benjamini Y. More powerful procedures for multiple significance testing. Stat Med. 1990;9(7):811\u20138.","journal-title":"Stat Med"},{"issue":"4","key":"12093_CR41","doi-asserted-by":"publisher","first-page":"2082","DOI":"10.1109\/TCYB.2020.3006240","volume":"52","author":"Z Yang","year":"2020","unstructured":"Yang Z, Wu N, Liang Y, Zhang H, Ren Y. SMSPL: robust multimodal approach to integrative analysis of multiomics data. IEEE Trans Cybern. 2020;52(4):2082\u201395.","journal-title":"IEEE Trans Cybern"},{"key":"12093_CR42","doi-asserted-by":"crossref","unstructured":"Hamid MHA, Yusoff M, Mohamed A. Survey on highly imbalanced multi-class data. Int J Adv Comput Sci Appl. 2022;13(6):211\u201329.","DOI":"10.14569\/IJACSA.2022.0130627"},{"issue":"3","key":"12093_CR43","first-page":"1560","volume":"14","author":"H Ali","year":"2019","unstructured":"Ali H, Salleh MM, Saedudin R, Hussain K, Mushtaq MF. Imbalance class problems in data mining: a review. Indones J Electr Eng Comput Sci. 2019;14(3):1560\u201371.","journal-title":"Indones J Electr Eng Comput Sci"},{"issue":"8","key":"12093_CR44","doi-asserted-by":"publisher","first-page":"2818","DOI":"10.1109\/TNNLS.2019.2913673","volume":"31","author":"B Krawczyk","year":"2019","unstructured":"Krawczyk B, Koziarski M, Wo\u017aniak M. Radial-based oversampling for multiclass imbalanced data classification. IEEE Trans Neural Netw Learn Syst. 2019;31(8):2818\u201331.","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"5","key":"12093_CR45","first-page":"1","volume":"1","author":"Y Sasaki","year":"2007","unstructured":"Sasaki Y, et al. The truth of the F-measure. Teach Tutor Mater. 2007;1(5):1\u20135.","journal-title":"Teach Tutor Mater"},{"key":"12093_CR46","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321\u201357.","journal-title":"J Artif Intell Res"},{"key":"12093_CR47","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/s13748-020-00219-x","volume":"9","author":"N Ghatasheh","year":"2020","unstructured":"Ghatasheh N, Faris H, Abukhurma R, Castillo PA, Al-Madi N, Mora AM, et al. Cost-sensitive ensemble methods for bankruptcy prediction in a highly imbalanced data distribution: a real case from the Spanish market. Prog Artif Intell. 2020;9:361\u201375.","journal-title":"Prog Artif Intell"},{"key":"12093_CR48","doi-asserted-by":"crossref","unstructured":"Loezer L, Enembreck F, Barddal JP, de\u00a0Souza Britto\u00a0Jr A. Cost-sensitive learning for imbalanced data streams. In: Proceedings of the 35th annual ACM symposium on applied computing. ACM; 2020. p. 498\u2013504.","DOI":"10.1145\/3341105.3373949"},{"key":"12093_CR49","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. 2001;45:5\u201332.","journal-title":"Mach Learn"},{"key":"12093_CR50","unstructured":"Andrews S, Tsochantaridis I, Hofmann T. Support vector machines for multiple-instance learning. Adv Neural Inf Process Syst. 2002;15:577\u201384."},{"issue":"3","key":"12093_CR51","first-page":"61","volume":"10","author":"J Platt","year":"1999","unstructured":"Platt J, et al. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv Large Margin Classifiers. 1999;10(3):61\u201374.","journal-title":"Adv Large Margin Classifiers"},{"issue":"2","key":"12093_CR52","doi-asserted-by":"publisher","DOI":"10.4249\/scholarpedia.1883","volume":"4","author":"LE Peterson","year":"2009","unstructured":"Peterson LE. K-nearest neighbor. Scholarpedia. 2009;4(2):1883.","journal-title":"Scholarpedia"},{"key":"12093_CR53","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine learning in Python. J Mach Learn Res. 2011;12:2825\u201330.","journal-title":"J Mach Learn Res"},{"key":"12093_CR54","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM. 2016:785\u201394.","DOI":"10.1145\/2939672.2939785"},{"key":"12093_CR55","unstructured":"Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, et\u00a0al. LightGBM: A highly efficient gradient boosting decision tree. In: Advances in neural information processing systems. PMLR; 2017. p. 3146\u201354."},{"issue":"5","key":"12093_CR56","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1089\/omi.2011.0118","volume":"16","author":"G Yu","year":"2012","unstructured":"Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics J Integr Biol. 2012;16(5):284\u20137.","journal-title":"Omics J Integr Biol"},{"issue":"6","key":"12093_CR57","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1016\/j.cell.2005.08.029","volume":"122","author":"U Stelzl","year":"2005","unstructured":"Stelzl U, Worm U, Lalowski M, Haenig C, Brembeck FH, Goehler H, et al. A human protein-protein interaction network: a resource for annotating the proteome. Cell. 2005;122(6):957\u201368.","journal-title":"Cell"},{"issue":"11","key":"12093_CR58","doi-asserted-by":"publisher","first-page":"1422","DOI":"10.1093\/bioinformatics\/btp163","volume":"25","author":"PJ Cock","year":"2009","unstructured":"Cock PJ, Antao T, Chang JT, Chapman BA, Cox CJ, Dalke A, et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics. 2009;25(11):1422.","journal-title":"Bioinformatics"},{"issue":"D1","key":"12093_CR59","doi-asserted-by":"publisher","first-page":"D118","DOI":"10.1093\/nar\/gkab1085","volume":"50","author":"H Lai","year":"2022","unstructured":"Lai H, Li Y, Zhang H, Hu J, Liao J, Su Y, et al. exoRBase 2.0: an atlas of mRNA, lncRNA and circRNA in extracellular vesicles from human biofluids. Nucleic Acids Res. 2022;50(D1):D118\u201328.","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"12093_CR60","first-page":"17","volume":"73","author":"RL Siegel","year":"2023","unstructured":"Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17\u201348.","journal-title":"CA Cancer J Clin"},{"issue":"4","key":"12093_CR61","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1001\/jama.2013.284664","volume":"311","author":"NA Schultz","year":"2014","unstructured":"Schultz NA, Dehlendorff C, Jensen BV, Bjerregaard JK, Nielsen KR, Bojesen SE, et al. Microrna biomarkers in whole blood for detection of pancreatic cancer. JAMA. 2014;311(4):392\u2013404.","journal-title":"JAMA"},{"issue":"5","key":"12093_CR62","doi-asserted-by":"publisher","first-page":"754","DOI":"10.1016\/j.pan.2017.07.001","volume":"17","author":"P Moutinho-Ribeiro","year":"2017","unstructured":"Moutinho-Ribeiro P, Coelho R, Giovannini M, Macedo G. Pancreatic cancer screening: still a delusion? Pancreatology. 2017;17(5):754\u201365.","journal-title":"Pancreatology"},{"key":"12093_CR63","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1200\/EDBK_175222","volume":"37","author":"DP Sohal","year":"2017","unstructured":"Sohal DP, Willingham FF, Falconi M, Raphael KL, Crippa S. Pancreatic adenocarcinoma: improving prevention and survivorship. Am Soc Clin Oncol Educ Book. 2017;37:301\u201310.","journal-title":"Am Soc Clin Oncol Educ Book"},{"issue":"19","key":"12093_CR64","doi-asserted-by":"publisher","first-page":"3267","DOI":"10.1158\/0008-5472.CAN-23-3998","volume":"84","author":"G Xu","year":"2024","unstructured":"Xu G, Huang R, Wumaier R, Lyu J, Huang M, Zhang Y, et al. Proteomic profiling of serum extracellular vesicles identifies diagnostic signatures and therapeutic targets in breast cancer. Cancer Res. 2024;84(19):3267\u201385.","journal-title":"Cancer Res"},{"issue":"11","key":"12093_CR65","doi-asserted-by":"publisher","DOI":"10.1002\/pmic.202300211","volume":"24","author":"J Cross","year":"2024","unstructured":"Cross J, Rai A, Fang H, Claridge B, Greening DW. Rapid and in-depth proteomic profiling of small extracellular vesicles for ultralow samples. Proteomics. 2024;24(11):2300211.","journal-title":"Proteomics"},{"issue":"1","key":"12093_CR66","doi-asserted-by":"publisher","first-page":"8991","DOI":"10.1038\/s41598-019-45473-9","volume":"9","author":"O Gidl\u00f6f","year":"2019","unstructured":"Gidl\u00f6f O, Evander M, Rezeli M, Marko-Varga G, Laurell T, Erlinge D. Proteomic profiling of extracellular vesicles reveals additional diagnostic biomarkers for myocardial infarction compared to plasma alone. Sci Rep. 2019;9(1):8991.","journal-title":"Sci Rep"},{"key":"12093_CR67","doi-asserted-by":"publisher","first-page":"RP87369","DOI":"10.7554\/eLife.87369.3","volume":"12","author":"B Bockorny","year":"2024","unstructured":"Bockorny B, Muthuswamy L, Huang L, Hadisurya M, Lim CM, Tsai LL, et al. A large-scale proteomics resource of circulating extracellular vesicles for biomarker discovery in pancreatic cancer. Elife. 2024;12:RP87369.","journal-title":"Elife"},{"issue":"3","key":"12093_CR68","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1164\/rccm.200503-483OE","volume":"172","author":"TE King Jr","year":"2005","unstructured":"King TE Jr. Clinical advances in the diagnosis and therapy of the interstitial lung diseases. Am J Respir Crit Care Med. 2005;172(3):268\u201379.","journal-title":"Am J Respir Crit Care Med"},{"key":"12093_CR69","doi-asserted-by":"publisher","first-page":"135","DOI":"10.55563\/clinexprheumatol\/mccyj0","volume":"40","author":"X Wang","year":"2022","unstructured":"Wang X, Zhu G, Ren Q, Wu J, Gu B, Su D, et al. Increased interleukin-11 associated with disease activity and development of interstitial lung disease in patients with rheumatoid arthritis. Clin Exp Rheumatol. 2022;40:135\u201341.","journal-title":"Clin Exp Rheumatol"},{"issue":"2","key":"12093_CR70","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1080\/03009742.2018.1477989","volume":"48","author":"T Matsuo","year":"2019","unstructured":"Matsuo T, Hashimoto M, Ito I, Kubo T, Uozumi R, Furu M, et al. Interleukin-18 is associated with the presence of interstitial lung disease in rheumatoid arthritis: a cross-sectional study. Scand J Rheumatol. 2019;48(2):87\u201394.","journal-title":"Scand J Rheumatol"},{"issue":"4","key":"12093_CR71","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1111\/1756-185X.14084","volume":"24","author":"MS Hussein","year":"2021","unstructured":"Hussein MS, El-Barbary AM, Nada DW, Gaber RA, Elkolaly RM, Aboelhawa MA. Identification of serum interleukin-13 and interleukin-13 receptor subunit expressions: rheumatoid arthritis-associated interstitial lung disease. Int J Rheum Dis. 2021;24(4):591\u20138.","journal-title":"Int J Rheum Dis"},{"key":"12093_CR72","doi-asserted-by":"crossref","unstructured":"Huang Y, Ma SF, Oldham JM, Adegunsoye A, Zhu D, Murray S, et\u00a0al. Machine Learning of Plasma Proteomics Classifies Diagnosis of Interstitial Lung Disease. Am J Respir Crit Care Med. 2024;210(4):444\u201354.\u00a0","DOI":"10.1164\/rccm.202309-1692OC"},{"issue":"6","key":"12093_CR73","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.3390\/cancers12061443","volume":"12","author":"H Kim","year":"2020","unstructured":"Kim H, Kang KN, Shin YS, Byun Y, Han Y, Kwon W, et al. Biomarker panel for the diagnosis of pancreatic ductal adenocarcinoma. Cancers. 2020;12(6):1443.","journal-title":"Cancers"},{"key":"12093_CR74","doi-asserted-by":"crossref","unstructured":"Lee DH, Yoon W, Lee A, Han Y, Byun Y, Kang JS, et\u00a0al. Multi-Biomarker Panel Prediction Model for Diagnosis of Pancreatic Cancer. J Hepatobiliary Pancreat Sci.\u00a02023;30(1):122\u201332.","DOI":"10.1002\/jhbp.986"},{"key":"12093_CR75","doi-asserted-by":"publisher","first-page":"1117","DOI":"10.1007\/s00432-013-1422-4","volume":"139","author":"J Chen","year":"2013","unstructured":"Chen J, Chen LJ, Xia YL, Zhou HC, Yang RB, Wu W, et al. Identification and verification of transthyretin as a potential biomarker for pancreatic ductal adenocarcinoma. J Cancer Res Clin Oncol. 2013;139:1117\u201327.","journal-title":"J Cancer Res Clin Oncol"},{"issue":"54","key":"12093_CR76","doi-asserted-by":"publisher","first-page":"93117","DOI":"10.18632\/oncotarget.21861","volume":"8","author":"J Park","year":"2017","unstructured":"Park J, Choi Y, Namkung J, Yi SG, Kim H, Yu J, et al. Diagnostic performance enhancement of pancreatic cancer using proteomic multimarker panel. Oncotarget. 2017;8(54):93117.","journal-title":"Oncotarget"},{"key":"12093_CR77","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2164-14-S8-S1","volume":"14","author":"W Zhang","year":"2013","unstructured":"Zhang W, Wan YW, Allen GI, Pang K, Anderson ML, Liu Z. Molecular pathway identification using biological network-regularized logistic models. BMC Genomics. 2013;14:1\u20138.","journal-title":"BMC Genomics"},{"issue":"6","key":"12093_CR78","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3457607","volume":"54","author":"N Mehrabi","year":"2021","unstructured":"Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A. A survey on bias and fairness in machine learning. ACM Comput Surv (CSUR). 2021;54(6):1\u201335.","journal-title":"ACM Comput Surv (CSUR)"},{"key":"12093_CR79","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13059-017-1215-1","volume":"18","author":"Y Hasin","year":"2017","unstructured":"Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18:1\u201315.","journal-title":"Genome Biol"},{"key":"12093_CR80","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/bs.adgen.2015.11.004","volume":"93","author":"YV Sun","year":"2016","unstructured":"Sun YV, Hu YJ. Integrative analysis of multi-omics data for discovery and functional studies of complex human diseases. Adv Genet. 2016;93:147\u201390.","journal-title":"Adv Genet"},{"key":"12093_CR81","doi-asserted-by":"crossref","unstructured":"Panigrahi S, Nanda A, Swarnkar T. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering. IEEE. 2009;22(10):1345\u201359.","DOI":"10.1109\/TKDE.2009.191"}],"updated-by":[{"DOI":"10.1186\/s12864-025-12240-2","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T00:00:00Z","timestamp":1761177600000}}],"container-title":["BMC Genomics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12864-025-12093-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12864-025-12093-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12864-025-12093-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T11:03:28Z","timestamp":1761217408000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcgenomics.biomedcentral.com\/articles\/10.1186\/s12864-025-12093-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,29]]},"references-count":81,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["12093"],"URL":"https:\/\/doi.org\/10.1186\/s12864-025-12093-9","relation":{},"ISSN":["1471-2164"],"issn-type":[{"value":"1471-2164","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,29]]},"assertion":[{"value":"9 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 September 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 October 2025","order":5,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":6,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1186\/s12864-025-12240-2","URL":"https:\/\/doi.org\/10.1186\/s12864-025-12240-2","order":8,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The self-collected data used in this study were obtained from an independent research project. The study adhered to ethical standards and received approval from the Institutional Review Board (IRB). Informed consent was obtained from all participants before the collection of blood samples. To protect participant confidentiality, all samples were anonymized, and data management was carried out in accordance with relevant privacy regulations. This research was carried out in accordance with the ethical principles of the Declaration of Helsinki. The study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University (Approval ID: ES-2023-129-02). Informed consent was obtained from all participants or their legal guardians.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"872"}}