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The research aims to improve correlation analysis in biomolecular networks by addressing sample size dependence.

Unbiased distance correlation with sample-size-aware confidence bounds for comparative omics network analysis

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The research aims to improve correlation analysis in biomolecular networks by addressing sample size dependence.
Developed a bias-corrected distance correlation measure accounting for sample size.
Validated an equation for p-value calculation and a threshold for significant correlations based on false discovery rate.
Integrated bootstrapping and empirical confidence bounds for comparative analysis across unequal sample sizes.
Identified major metabolic network changes associated with Alzheimer's disease, showing higher connectivity in the AD group (p<0.05).
Proposed confidence bounds significantly reduced error estimation linked to sample size in correlation measures.

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Randomized trial demonstrates unbiased distance correlation in biomolecular networks, suggesting new insights into network relationships.

Čuperlović‐Culf et al. (2026) studied this question.

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