{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T13:46:16Z","timestamp":1792590376858,"version":"4.5.0"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:00:00Z","timestamp":1759536000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:00:00Z","timestamp":1759536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["NRF-2022R1A2C1092497"],"award-info":[{"award-number":["NRF-2022R1A2C1092497"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Genes Genom"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s13258-025-01688-x","type":"journal-article","created":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T09:27:35Z","timestamp":1759570055000},"page":"1175-1185","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["PSM-SMOTE: propensity score matching and synthetic minority oversampling for handling unbalanced microbiome data"],"prefix":"10.1007","volume":"47","author":[{"given":"Jeongsup","family":"Moon","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8294-590X","authenticated-orcid":false,"given":"Taesung","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,4]]},"reference":[{"key":"1688_CR1","doi-asserted-by":"crossref","unstructured":"Wensel CR, Pluznick JL, Salzberg SL, Sears CL (2022) Next-generation sequencing: insights to advance clinical investigations of the Microbiome. J Clin Invest. 132(7)","DOI":"10.1172\/JCI154944"},{"issue":"7122","key":"1688_CR2","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1038\/nature05414","volume":"444","author":"PJ Turnbaugh","year":"2006","unstructured":"Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI (2006) An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444(7122):1027\u20131031","journal-title":"Nature"},{"issue":"6","key":"1688_CR3","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.1016\/j.cell.2016.11.018","volume":"167","author":"TR Sampson","year":"2016","unstructured":"Sampson TR, Debelius JW, Thron T, Janssen S, Shastri GG, Ilhan ZE et al (2016) Gut microbiota regulate motor deficits and neuroinflammation in a model of Parkinson\u2019s disease. Cell 167(6):1469\u20131480 e12","journal-title":"Cell"},{"issue":"7351","key":"1688_CR4","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1038\/nature10213","volume":"474","author":"AL Kau","year":"2011","unstructured":"Kau AL, Ahern PP, Griffin NW, Goodman AL, Gordon JI (2011) Human nutrition, the gut microbiome and the immune system. Nature 474(7351):327\u2013336","journal-title":"Nature"},{"issue":"5","key":"1688_CR5","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.tins.2013.01.005","volume":"36","author":"JA Foster","year":"2013","unstructured":"Foster JA, McVey Neufeld KA (2013) Gut-brain axis: how the Microbiome influences anxiety and depression. Trends Neurosci 36(5):305\u2013312","journal-title":"Trends Neurosci"},{"issue":"6371","key":"1688_CR6","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1126\/science.aan4236","volume":"359","author":"V Gopalakrishnan","year":"2018","unstructured":"Gopalakrishnan V, Spencer CN, Nezi L, Reuben A, Andrews MC, Karpinets TV et al (2018) Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 359(6371):97\u2013103","journal-title":"Science"},{"key":"1688_CR7","doi-asserted-by":"crossref","unstructured":"Kim SI, Kang N, Leem S, Yang J, Jo H, Lee M et al (2020) Metagenomic analysis of serum microbe-derived extracellular vesicles and diagnostic models to differentiate ovarian cancer and benign ovarian tumor. Cancers (Basel) 12(5)","DOI":"10.3390\/cancers12051309"},{"key":"1688_CR8","doi-asserted-by":"crossref","unstructured":"Kim JR, Han K, Han Y, Kang N, Shin TS, Park HJ et al (2021) Microbiome markers of pancreatic cancer based on Bacteria-Derived extracellular vesicles acquired from blood samples: a retrospective propensity score matching analysis. Biology (Basel) 10(3)","DOI":"10.3390\/biology10030219"},{"key":"1688_CR9","doi-asserted-by":"crossref","unstructured":"Kim B, Cho EJ, Yoon JH, Kim SS, Cheong JY, Cho SW et al (2020) Pathway-based integrative analysis of metabolome and microbiome data from hepatocellular carcinoma and liver cirrhosis patients. Cancers (Basel) 12(9)","DOI":"10.3390\/cancers12092705"},{"key":"1688_CR10","doi-asserted-by":"crossref","unstructured":"Jeong JY, Kim TB, Kim J, Choi HW, Kim EJ, Yoo HJ et al (2020) Diversity in the extracellular vesicle-derived microbiome of tissues according to tumor progression in pancreatic cancer. Cancers (Basel) 12(9)","DOI":"10.3390\/cancers12092346"},{"issue":"2","key":"1688_CR11","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1177\/0962280211416038","volume":"24","author":"JD Ciolino","year":"2015","unstructured":"Ciolino JD, Martin RH, Zhao W, Hill MD, Jauch EC, Palesch YY (2015) Measuring continuous baseline covariate imbalances in clinical trial data. Stat Methods Med Res 24(2):255\u2013272","journal-title":"Stat Methods Med Res"},{"issue":"5","key":"1688_CR12","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.compbiomed.2010.03.005","volume":"40","author":"DC Li","year":"2010","unstructured":"Li DC, Liu CW, Hu SC (2010) A learning method for the class imbalance problem with medical data sets. Comput Biol Med 40(5):509\u2013518","journal-title":"Comput Biol Med"},{"key":"1688_CR13","doi-asserted-by":"crossref","unstructured":"Li Y, Hsu WW (2022) Alzheimer\u2019s Disease Neuroimaging I. A classification for complex imbalanced data in disease screening and early diagnosis. Stat Med","DOI":"10.1002\/sim.9442"},{"issue":"4","key":"1688_CR14","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/j.resuscitation.2011.12.006","volume":"83","author":"P Walsh","year":"2012","unstructured":"Walsh P, Shanholtzer L, Loewen M, Trinh K, McEnulty B, Rothenberg SJ (2012) A matched case control study with propensity score balancing examining the protective effect of paracetamol against parentally reported Apnoea in infants. Resuscitation 83(4):440\u2013446","journal-title":"Resuscitation"},{"key":"1688_CR15","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1186\/1471-2288-12-70","volume":"12","author":"R Pirracchio","year":"2012","unstructured":"Pirracchio R, Resche-Rigon M, Chevret S (2012) Evaluation of the propensity score methods for estimating marginal odds ratios in case of small sample size. BMC Med Res Methodol 12:70","journal-title":"BMC Med Res Methodol"},{"key":"1688_CR16","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.3389\/fneur.2020.01041","volume":"11","author":"A Cosma-Grigorov","year":"2020","unstructured":"Cosma-Grigorov A, Meixner H, Mrochen A, Wirtz S, Winkler J, Marxreiter F (2020) Changes in gastrointestinal microbiome composition in PD: a pivotal role of covariates. Front Neurol 11:1041","journal-title":"Front Neurol"},{"key":"1688_CR17","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1613\/jair.1.11192","volume":"61","author":"A Fern\u00e1ndez","year":"2018","unstructured":"Fern\u00e1ndez A, Garc\u00eda S, Herrera F, Chawla NV (2018) SMOTE for learning from imbalanced data: progress and Challenges, marking the 15-year anniversary. J Artif Intell Res 61:863\u2013905","journal-title":"J Artif Intell Res"},{"key":"1688_CR18","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2011) SMOTE: synthetic minority over-sampling technique"},{"key":"1688_CR19","doi-asserted-by":"crossref","unstructured":"Han H, Wang W-Y, Mao B-H (eds) Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning 2005. Springer, Berlin, Heidelberg","DOI":"10.1007\/11538059_91"},{"key":"1688_CR20","doi-asserted-by":"crossref","unstructured":"Rivera WA, Goel A, Kincaid JP (2014) OUPS: a combined approach using SMOTE and propensity score matching. In: 13th international conference on machine learning and applications. IEEE, pp 424\u2013427","DOI":"10.1109\/ICMLA.2014.106"},{"issue":"3","key":"1688_CR21","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273\u2013297","journal-title":"Mach Learn"},{"key":"1688_CR22","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (eds) (2016) 2016 XGBoost: a scalable tree boosting system. In: International conference on knowledge discovery and data mining. ACM, Ithaca","DOI":"10.1145\/2939672.2939785"},{"issue":"1","key":"1688_CR23","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"issue":"12","key":"1688_CR24","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1007\/s11306-019-1612-4","volume":"15","author":"KM Mendez","year":"2019","unstructured":"Mendez KM, Reinke SN, Broadhurst DI (2019) A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification. Metabolomics 15(12):150","journal-title":"Metabolomics"},{"key":"1688_CR25","doi-asserted-by":"crossref","unstructured":"Lee HC, Yoon SB, Yang SM, Kim WH, Ryu HG, Jung CW et al (2018) Prediction of acute kidney injury after liver transplantation: machine learning approaches vs. Logistic regression model. J Clin Med 7(11)","DOI":"10.3390\/jcm7110428"},{"key":"1688_CR26","doi-asserted-by":"crossref","unstructured":"Lee HC, Yoon HK, Nam K, Cho YJ, Kim TK, Kim WH et al (2018) Derivation and validation of machine learning approaches to predict acute kidney injury after cardiac surgery. J Clin Med 7(10)","DOI":"10.3390\/jcm7100322"},{"key":"1688_CR27","doi-asserted-by":"crossref","first-page":"39707","DOI":"10.1109\/ACCESS.2021.3064084","volume":"9","author":"A Ishaq","year":"2021","unstructured":"Ishaq A, Sadiq S, Umer M, Ullah S, Mirjalili S, Rupapara V et al (2021) Improving the prediction of heart failure patients\u2019 survival using SMOTE and effective data mining techniques. IEEE Access 9:39707\u201339716","journal-title":"IEEE Access"},{"key":"1688_CR28","first-page":"1","volume":"8","author":"R Ghorbani","year":"2020","unstructured":"Ghorbani R, Ghousi R (2020) Comparing different resampling methods in predicting students\u2019 performance using machine learning techniques. IEEE Access 8:1","journal-title":"IEEE Access"},{"issue":"7","key":"1688_CR29","doi-asserted-by":"crossref","first-page":"e0179805","DOI":"10.1371\/journal.pone.0179805","volume":"12","author":"M Alghamdi","year":"2017","unstructured":"Alghamdi M, Al-Mallah M, Keteyian S, Brawner C, Ehrman J, Sakr S (2017) Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: the Henry Ford exercise testing (FIT) project. PLoS ONE 12(7):e0179805\u2013e","journal-title":"PLoS ONE"},{"issue":"13","key":"1688_CR30","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1093\/bioinformatics\/btl158","volume":"22","author":"W Li","year":"2006","unstructured":"Li W, Godzik A (2006) Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22(13):1658\u20131659","journal-title":"Bioinformatics"},{"issue":"5","key":"1688_CR31","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1038\/nmeth.f.303","volume":"7","author":"R Knight","year":"2010","unstructured":"Knight R, Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD et al (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7(5):335\u2013336","journal-title":"Nat Methods"},{"issue":"19","key":"1688_CR32","doi-asserted-by":"crossref","first-page":"2460","DOI":"10.1093\/bioinformatics\/btq461","volume":"26","author":"RC Edgar","year":"2010","unstructured":"Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26(19):2460\u20132461","journal-title":"Bioinformatics"},{"issue":"1","key":"1688_CR33","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1186\/s13073-016-0290-3","volume":"8","author":"NT Baxter","year":"2016","unstructured":"Baxter NT, Ruffin MTt, Rogers MA, Schloss PD (2016) Microbiota-based model improves the sensitivity of fecal immunochemical test for detecting colonic lesions. Genome Med 8(1):37","journal-title":"Genome Med"},{"issue":"23","key":"1688_CR34","doi-asserted-by":"crossref","first-page":"7537","DOI":"10.1128\/AEM.01541-09","volume":"75","author":"PD Schloss","year":"2009","unstructured":"Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB et al (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75(23):7537\u20137541","journal-title":"Appl Environ Microbiol"},{"issue":"16","key":"1688_CR35","doi-asserted-by":"crossref","first-page":"5261","DOI":"10.1128\/AEM.00062-07","volume":"73","author":"Q Wang","year":"2007","unstructured":"Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Na\u00efve bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73(16):5261\u20135267","journal-title":"Appl Environ Microbiol"},{"issue":"5","key":"1688_CR36","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1016\/j.cell.2015.11.001","volume":"163","author":"D Zeevi","year":"2015","unstructured":"Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A et al (2015) Personalized nutrition by prediction of glycemic responses. Cell 163(5):1079\u20131094","journal-title":"Cell"},{"issue":"10","key":"1688_CR37","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1038\/nmeth.2604","volume":"10","author":"RC Edgar","year":"2013","unstructured":"Edgar RC (2013) UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods 10(10):996\u2013998","journal-title":"Nat Methods"},{"issue":"12","key":"1688_CR38","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1038\/nmeth.2221","volume":"9","author":"S Marco-Sola","year":"2012","unstructured":"Marco-Sola S, Sammeth M, Guigo R, Ribeca P (2012) The GEM mapper: fast, accurate and versatile alignment by filtration. Nat Methods 9(12):1185\u20131188","journal-title":"Nat Methods"},{"issue":"2","key":"1688_CR39","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1086\/224936","volume":"76","author":"RP Althauser","year":"1970","unstructured":"Althauser RP, Rubin D (1970) The computerized construction of a matched sample. Am J Sociol 76(2):325\u2013346","journal-title":"Am J Sociol"},{"issue":"1","key":"1688_CR40","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1093\/bioinformatics\/btp616","volume":"26","author":"MD Robinson","year":"2010","unstructured":"Robinson MD, McCarthy DJ, Smyth GK (2010) EdgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139\u2013140","journal-title":"Bioinformatics"},{"issue":"2","key":"1688_CR41","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1089\/cmb.2015.0157","volume":"23","author":"X Peng","year":"2016","unstructured":"Peng X, Li G, Liu Z (2016) Zero-Inflated beta regression for differential abundance analysis with metagenomics data. J Comput Biol 23(2):102\u2013110","journal-title":"J Comput Biol"},{"issue":"12","key":"1688_CR42","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1038\/nmeth.2658","volume":"10","author":"JN Paulson","year":"2013","unstructured":"Paulson JN, Stine OC, Bravo HC, Pop M (2013) Differential abundance analysis for microbial marker-gene surveys. Nat Methods 10(12):1200\u20131202","journal-title":"Nat Methods"},{"key":"1688_CR43","first-page":"27663","volume":"26","author":"S Mandal","year":"2015","unstructured":"Mandal S, Van Treuren W, White RA, Eggesbo M, Knight R, Peddada SD (2015) Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis 26:27663","journal-title":"Microb Ecol Health Dis"},{"issue":"12","key":"1688_CR44","doi-asserted-by":"crossref","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","journal-title":"Genome Biol"},{"key":"1688_CR45","volume-title":"Comparison study of statistical methods for the analysis of metagenome data","author":"CY Lee","year":"2018","unstructured":"Lee CY (2018) Comparison study of statistical methods for the analysis of metagenome data. Seoul National University, Seoul"},{"issue":"1","key":"1688_CR46","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","volume":"57","author":"Y Benjamini","year":"1995","unstructured":"Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Stat Soc Ser B Methodological 57(1):289\u2013300","journal-title":"J Royal Stat Soc Ser B Methodological"},{"issue":"4","key":"1688_CR47","first-page":"326","volume":"27","author":"JR Bray","year":"1957","unstructured":"Bray JR, Curtis JT (1957) An ordination of the upland forest communities of Southern Wisconsin. Ecol Monogr 27(4):326\u2013349","journal-title":"Ecol Monogr"},{"issue":"12","key":"1688_CR48","doi-asserted-by":"crossref","first-page":"8228","DOI":"10.1128\/AEM.71.12.8228-8235.2005","volume":"71","author":"C Lozupone","year":"2005","unstructured":"Lozupone C, Knight R (2005) UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol 71(12):8228\u20138235","journal-title":"Appl Environ Microbiol"},{"issue":"16","key":"1688_CR49","doi-asserted-by":"crossref","first-page":"2106","DOI":"10.1093\/bioinformatics\/bts342","volume":"28","author":"C Jun","year":"2012","unstructured":"Jun C, Bittinger K, Charlson ES, Hofifmann C, Lewis J, Wu GD et al (2012) Associating Microbiome composition with environmental covariates using generalized UniFrac distances. Bioinformatics 28(16):2106\u20132113","journal-title":"Bioinformatics"},{"issue":"5","key":"1688_CR50","doi-asserted-by":"crossref","first-page":"1576","DOI":"10.1128\/AEM.01996-06","volume":"73","author":"CA Lozupone","year":"2007","unstructured":"Lozupone CA, Hamady M, Kelley ST, Knight R (2007) Quantitative and qualitative \u03b2 diversity measures lead to different insights into factors that structure microbial communities. Appl Environ Microbiol 73(5):1576\u20131585","journal-title":"Appl Environ Microbiol"},{"issue":"4","key":"1688_CR51","doi-asserted-by":"crossref","first-page":"3205","DOI":"10.1007\/s13369-019-04336-1","volume":"45","author":"H Al Majzoub","year":"2020","unstructured":"Al Majzoub H, Elgedawy I, Akayd\u0131n \u00d6, K\u00f6se Uluk\u00f6k M (2020) HCAB-SMOTE: A hybrid clustered affinitive borderline SMOTE approach for imbalanced data binary classification. Arab J Sci Eng (2011) 45(4):3205\u20133222","journal-title":"Arab J Sci Eng (2011)"},{"key":"1688_CR52","doi-asserted-by":"crossref","unstructured":"He HB, Bai Y, Garcia EA, Li ST (2008) ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE international joint conference on neural networks, vol 1\u20138, pp 1322\u20131328","DOI":"10.1109\/IJCNN.2008.4633969"},{"issue":"4","key":"1688_CR53","doi-asserted-by":"crossref","first-page":"3205","DOI":"10.1007\/s13369-019-04336-1","volume":"45","author":"H Al Majzoub","year":"2020","unstructured":"Al Majzoub H, Elgedawy I, Akaydin O, Ulukok MK (2020) HCAB-SMOTE: a hybrid clustered affinitive borderline SMOTE approach for imbalanced data binary classification. Arab J Sci Eng 45(4):3205\u20133222","journal-title":"Arab J Sci Eng"},{"key":"1688_CR54","first-page":"1","volume":"PP","author":"Q Ning","year":"2021","unstructured":"Ning Q, Zhao X, Ma Z (2021) A novel method for identification of glutarylation sites combining Borderline-SMOTE with Tomek links technique in imbalanced data. IEEE\/ACM Trans Comput Biol Bioinf PP:1","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"issue":"3","key":"1688_CR55","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1007\/s11277-021-08690-y","volume":"121","author":"M Revathi","year":"2021","unstructured":"Revathi M, Ramyachitra D (2021) A modified borderline Smote with noise reduction in imbalanced datasets. Wireless Pers Commun 121(3):1659\u20131680","journal-title":"Wireless Pers Commun"},{"key":"1688_CR56","doi-asserted-by":"crossref","first-page":"105662","DOI":"10.1016\/j.asoc.2019.105662","volume":"83","author":"G Kov\u00e1cs","year":"2019","unstructured":"Kov\u00e1cs G (2019) An empirical comparison and evaluation of minority oversampling techniques on a large number of imbalanced datasets. Appl Soft Comput 83:105662","journal-title":"Appl Soft Comput"}],"container-title":["Genes &amp; Genomics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13258-025-01688-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13258-025-01688-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13258-025-01688-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T08:23:19Z","timestamp":1761898999000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13258-025-01688-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,4]]},"references-count":56,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["1688"],"URL":"https:\/\/doi.org\/10.1007\/s13258-025-01688-x","relation":{},"ISSN":["1976-9571","2092-9293"],"issn-type":[{"value":"1976-9571","type":"print"},{"value":"2092-9293","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,4]]},"assertion":[{"value":"6 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The PDAC dataset were reviewed and approved by Institutional Review Board at Seoul National University Hospital (IRB No. 1601-137-739). Obesity dataset was Approved by Tel Aviv Sourasky Medical Center Institutional Review Board (IRB), approval numbers TLV-0658-12, TLV-0050-13 and TLV-0522-10; Kfar Shaul Hospital IRB, approval number 0\u201373; and Weizmann Institute of Science Bioethics and Embryonic Stem Cell Research oversight committee. Reported to\n                      \n                      , NCT: NCT01892956.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The informed consent was waived under IRB approval. This study is a retrospective designed study using only data from blood samples that have already been obtained.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}