Integration of three machine learning algorithms to characterize lipid metabolism-associated hub genes in sepsis and atrial fibrillation.

(A): Lasso regression coefficient path plot for GSE28750 dataset, showing the relationship between Log Lambda and variable coefficients. (B): Cross-validation deviance plot for GSE28750 Lasso regression, depicting Log(λ) values and binomial deviance. &l...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Changze Ou (22806374) (author)
مؤلفون آخرون:	Haidong Yu (101810) (author), Binbin Chen (504446) (author), Huajun Long (22806377) (author)
منشور في:	2025
الموضوعات:	Biochemistry Medicine Cell Biology Genetics Molecular Biology Developmental Biology Cancer Virology Computational Biology Biological Sciences not elsewhere classified weighted gene co systematic study based mitochondrial function regulation machine learning models machine learning methodologies lipid metabolism abnormalities influencing mitochondrial function genes primarily participate future research necessitates expression network analysis dimensional analytical approach core characteristic genes cell adhesion pathways shared molecular mechanisms atrial fibrillation datasets xlink "> sepsis xlink "> molecular mechanisms atrial fibrillation related datasets profound impact inflammatory responses functional experiments dpep2 emerging disease progression curve values critical roles critical role comprehensively elucidate coagulation cascade clinical validation
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Integration of three machine learning algorithms to characterize lipid metabolism-associated hub genes in sepsis and atrial fibrillation.

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