Supplemental Figure 6 from Utilizing Serum-Derived Lipidomics with Protein Biomarkers and Machine Learning for Early Detection of Ovarian Cancer in the Symptomatic Population

Key Points

Variable importance scores from PLSDA highlight key biomarkers for ovarian cancer detection, improving diagnostics.
The analysis focuses on the integration of lipidomics and protein biomarkers for early detection in symptomatic individuals.
Machine learning techniques enhance the evaluation of lipidomic profiles, leading to better identification of ovarian cancer.
Utilizing serum-derived data enhances predictive accuracy for ovarian cancer in clinical settings.

Analysis reveals PLSDA variable importance in identifying ovarian cancer in a symptomatic population, suggesting effective biomarker integration.

Supplemental Figure 6. PLSDA Variable Importance Scores across Cohorts and Comparisons

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Giles et al. (2025) studied this question.

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