ArticleScientific reports2025
Identifying ovarian cancer with machine learning DNA methylation pattern analysis.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- DNA methylation biomarkers for early detection of ovarian cancer.Molecular biology reports · 2026Review
- Integrative bioinformatics approaches for early detection biomarkers in ovarian cancer.Annals of medicine and surgery (2012) · 2026Review
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Authors and funding
7 authors.
Funding
Abstract
The majority of patients with epithelial ovarian cancer (EOC) continue to be diagnosed at an advanced stage despite great advances in this disease treatment. To impact overall survival, we need better methods of EOC early diagnosis. We performed a case control study to predict high-grade serous cancer (HGSC) using artificial intelligence methodology and methylated DNA from surgical specimens. Initial prediction models with MethylNet were accurate but complex (AUC = 100%). We optimized these models by selecting the most informative probes with univariate ANOVA analyses first, and then multivariate lasso regression modelling. This step-wise approach resulted in 9 methylated probes predicting HGSC with an AUC of 100%. These models were validated with different analytics and with an independent DNA-methylation experiment with excellent performances.
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Registered trials
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