Construction of nomogram model through SVM algorithm.

<p>(A) Boxplot of |residual| of three algorithms: SVM, GLM, and RF. (B) Reverse cumulative distribution of |residual| of three algorithms. (C) ROC curve indicating the accuracy of three algorithms. (D) Importance of significant m7G regulators based on three algorithms. (E) Nomogram model using...

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Bibliographic Details
Main Author:	Jiangli Zhao (2308330) (author)
Other Authors:	Qingyuan Sun (188592) (author), Xuchen Liu (13258842) (author), Jiwei Wang (508674) (author), Ning Yang (114965) (author), Chao Li (145513) (author), Xinyu Wang (341591) (author)
Published:	2025
Subjects:	Biochemistry Microbiology Cell Biology Molecular Biology Neuroscience Immunology Mental Health Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified sup >+</ sup nearly 1 %. machine learning algorithms gene expression analysis common brain disease causes different types attracted significant attention prevalent rna modification immune cell infiltration neuronal oxidative phosphorylation findings also suggested div >< p rna expression levels identified m7g regulators epilepsy patients confirmed oxidative phosphorylation m7g regulators immune microenvironment epilepsy patients rna n7 two subtypes tissue samples regulatory pattern recent research programmed death nomogram model m7g patterns m7g methylation integrative role insights gained incidence rate important role healthy controls datasets gse143272 cytolytic activity constructed based consensus clustering clinical significance cellular death
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Construction of nomogram model through SVM algorithm.

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