Characterization of biomarkers for PCOS.

<p>(A) Random forests calculate the importance of biomarkers. Abscissa: importance; ordinate: substance name. (B) Boxplots of the expression levels of PI (18:0/20:3)-H and PE (18:1p/22:6)-H in each sample. (C) Correlation analysis graph. Abscissa: substance name; ordinate: substance name; circ...

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Bibliographic Details
Main Author:	Ji-ying Chen (20521673) (author)
Other Authors:	Wu-jie Chen (20521676) (author), Zhi-ying Zhu (11885067) (author), Shi Xu (2182014) (author), Li-lan Huang (20521679) (author), Wen-qing Tan (20521682) (author), Yong-gang Zhang (20521685) (author), Yan-li Zhao (16487577) (author)
Published:	2025
Subjects:	Biochemistry Cell Biology Biotechnology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified support vector machine model &# 8217 machine learning algorithms ensemble machine learning 6 )- h 3 )- h differential lipid metabolites different lipid metabolites statistical test pre untargeted lipidomics analysis reliable biomarker screening better classification ability screen serum biomarkers untargeted lipidomics lipid omics test set serum biomarkers selected biomarkers screened biomarkers candidate biomarkers study demonstrated results indicated random forest logistic regression close effects clinical indexes auc value
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Characterization of biomarkers for PCOS.

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