Model robustness analysis and hyperparameter optimization

a. Evolution of Lung False Positive Rate (LFPR) across different RNAseq dropout rates during testing, with baseline Missing at Random (MAR) performance (green dashed line) and 95% confidence intervals (shaded area). b. Rank correlation comparison betwee...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Hakim Benkirane (14725564) (author)
مؤلفون آخرون:	Maria Vakalopoulou (21560771) (author), David Planchard (5843483) (author), Julien Adam (3497519) (author), Ken Olaussen (3497504) (author), Stefan Michiels (108030) (author), Paul-Henry Cournède (14004669) (author)
منشور في:	2025
الموضوعات:	Medicine Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified whole slide images introducing substantial correlations extend enrichment analysis different cancer types despite recent advances characterizing cancer presents based approach designed distinct biological levels within data sources oncology </ p div >< p weakly supervised tasks unravel pathway activation readily interpretable manner representing multimodal data supervised tasks robust manner gene levels spatial data missing data interpretable fusion histopathology data validation cohorts tumor ’ publicly available precision medicine novel deep multimodal relationships molecular profiling method lies medical domain interpretation scores interpretable multi help understand complex relationships also deals
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الوصف
الملخص:	<p><b>a.</b> Evolution of Lung False Positive Rate (LFPR) across different RNAseq dropout rates during testing, with baseline Missing at Random (MAR) performance (green dashed line) and 95% confidence intervals (shaded area). <b>b.</b> Rank correlation comparison between MoE and attention-based feature importance across three TCGA cancer types (BRCA, COAD, STAD) with error bars indicating standard deviation. <b>c.</b> Ablation studies showing model performance sensitivity to hypergraph construction parameters: similarity threshold (left) and kernel parameter ratio (right), with 95% confidence intervals (shaded areas).</p> <p>(TIF)</p>

Model robustness analysis and hyperparameter optimization

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