General characteristics of training and validation datasets.

General characteristics of training and validation datasets.

<p>General characteristics of training and validation datasets.</p>

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Bibliographic Details
Main Author:	Zhiwei Li (131853) (author)
Other Authors:	Leyi Wang (408009) (author), Shuting Yang (532494) (author), Bin Luo (303443) (author), Yezi Liu (20724872) (author), Mengsi Chen (21533639) (author), Changmin Wang (3403493) (author)
Published:	2025
Subjects:	Medicine Cell Biology Genetics Immunology Developmental Biology Cancer Hematology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified using lasso regression significantly worse prognoses multivariate cox regression findings provide insights expression network analysis exhibited poorer survival estimated mortality probabilities 096 &# 8211 085 &# 8211 weighted gene co identify gene modules risk scoring system improving clinical decision independent prognostic factor unsupervised cluster analysis risk groups using nk cells elevated genomes enrichment analyses estimated risk score 164 patients treated gene prognostic model study identified immune immune regulatory pathways independent validation cohort associated molecular classification related molecular subtypes hr ]: 1 ci ]: 1 sepsis </ p day ), 0 prognostic model independent cohort risk score risk groups molecular subtypes day ), gene ontology validation datasets study aims risk patients inflammatory pathways two subtypes day ). clinical parameters clinical outcomes immune status immune dysregulation xlink "> stratifying patients related genes
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Summary:	<p>General characteristics of training and validation datasets.</p>

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