ArticleInternational journal of molecular sciences2026
Machine Learning-Guided Multi-Cohort Transcriptomic Profiling Identifies
Article in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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Authors and funding
3 authors.
Funding
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Abstract
Reliable biomarkers for high-grade serous carcinoma (HGSC) with prognostic and microenvironmental relevance remain limited. Here, we developed a machine learning-guided cross-cohort transcriptomic framework to identify stable biomarkers in HGSC. Three GEO cohorts comprising 68 samples (34 HGSC and 34 normal) and 21,355 genes were integrated, and five classifiers were benchmarked under strict Leave-One-Dataset-Out (LODO) validation. Differential expression and random-effects meta-analysis were used to support cross-cohort feature prioritization, and external validation was performed in TCGA-OV tumors (
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Identifiers
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