{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Li J"],"funding":["Fujian Provincial Health Technology Project","the Joint Funds for the Innovation of Science and Technology, Fujian Province","National Natural Science Foundation of China"],"pagination":["2629088"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12915860"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["17(1)"],"pubmed_abstract":["Despite advances in antiviral therapy, the rate of functional cure for chronic hepatitis B (CHB) remains unsatisfactory, and developing an applicable prediction model is pivotal to improving it. Thus, we aimed to identify key predictive factors and develop prognostic models for functional cure in HBeAg-negative patients and the advantaged populations. This retrospective study included 202 HBeAg-negative CHB patients (114 classified as advantaged populations) receiving pegylated interferon-alpha (PEG-IFNα) therapy for model derivation and internal validation, and 183 HBeAg-negative CHB patients (117 classified as advantaged populations) for model external validation. Using 48 routinely collected clinical indicators, we constructed prediction models through LASSO regression followed by multi"],"journal":["Virulence"],"pubmed_title":["Machine learning prediction of functional cure to pegylated interferon-alpha therapy in two HBV populations: Advantaged populations and HBeAg-negative patients."],"pmcid":["PMC12915860"],"funding_grant_id":["2024CXA018","82472344","2023Y9018"],"pubmed_authors":["Yang X","Li H","Li J","Lin C","He Y","Xun Z","Jiang R","Zheng Q","Gong K","Wang L"],"additional_accession":[]},"is_claimable":false,"name":"Machine learning prediction of functional cure to pegylated interferon-alpha therapy in two HBV populations: Advantaged populations and HBeAg-negative patients.","description":"Despite advances in antiviral therapy, the rate of functional cure for chronic hepatitis B (CHB) remains unsatisfactory, and developing an applicable prediction model is pivotal to improving it. Thus, we aimed to identify key predictive factors and develop prognostic models for functional cure in HBeAg-negative patients and the advantaged populations. This retrospective study included 202 HBeAg-negative CHB patients (114 classified as advantaged populations) receiving pegylated interferon-alpha (PEG-IFNα) therapy for model derivation and internal validation, and 183 HBeAg-negative CHB patients (117 classified as advantaged populations) for model external validation. Using 48 routinely collected clinical indicators, we constructed prediction models through LASSO regression followed by multi","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Dec","modification":"2026-07-16T06:40:38.867Z","creation":"2026-07-09T10:40:34.777Z"},"accession":"S-EPMC12915860","cross_references":{"pubmed":["41656920"],"doi":["10.1080/21505594.2026.2629088"]}}