Identification of lipid metabolism-related biomarkers in familial hypercholesterolemia via integrated bioinformatics and machine learning approaches.

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Bibliographic Details
Title: Identification of lipid metabolism-related biomarkers in familial hypercholesterolemia via integrated bioinformatics and machine learning approaches.
Authors: Long L; School of Medicine, Kunming University of Science and Technology, Kunming, China., Zhu B; School of Medicine, Kunming University of Science and Technology, Kunming, China. baoshengzhu2024@163.com.; Department of Medical Genetics, National Health Commission Key Laboratory of Healthy Birth and Birth Defects Prevention and Control in Western China, Key Laboratory of Birth Defects and Genetic Diseases of Yunnan Province, The First People's Hospital of Yunnan Province, Kunming University of Science and Technology Affiliated Hospital, Kunming, China. baoshengzhu2024@163.com., Lv T; School of Medicine, Kunming University of Science and Technology, Kunming, China. taolv851109@126.com.; Department of Medical Genetics, National Health Commission Key Laboratory of Healthy Birth and Birth Defects Prevention and Control in Western China, Key Laboratory of Birth Defects and Genetic Diseases of Yunnan Province, The First People's Hospital of Yunnan Province, Kunming University of Science and Technology Affiliated Hospital, Kunming, China. taolv851109@126.com.
Source: Journal of cardiothoracic surgery [J Cardiothorac Surg] 2026 Apr 24; Vol. 21 (1). Date of Electronic Publication: 2026 Apr 24.
Publication Type: Journal Article
Journal Info: Publisher: BioMed Central Country of Publication: England NLM ID: 101265113 Publication Model: Electronic Cited Medium: Internet ISSN: 1749-8090 (Electronic) Linking ISSN: 17498090 NLM ISO Abbreviation: J Cardiothorac Surg Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1749-8090
DOI:10.1186/s13019-026-04107-8