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    Guidelines for correlation coefficient threshold settings in metabolite correlation networks exemplified on a potato association panel
    (BioMed Central Ltd, 2021-12-01)
    Correlation network analysis has become an integral tool to study metabolite datasets. Networks are constructed by omitting correlations between metabolites based on two thresholds—namely the r and the associated p-values. While p-value threshold settings follow the rules of multiple hypotheses testing correction, guidelines for r-value threshold settings have not been defined. Results: Here, we introduce a method that allows determining the r-value threshold based on an iterative approach, where different networks are constructed and their network topology is monitored. Once the network topology changes significantly, the threshold is set to the corresponding correlation coefficient value. The approach was exemplified on: (i) a metabolite and morphological trait dataset from a potato association panel, which was grown under normal irrigation and water recovery conditions; and validated (ii) on a metabolite dataset of hearts of fed and fasted mice. For the potato normal irrigation correlation network a threshold of Pearson’s |r|≥ 0.23 was suggested, while for the water recovery correlation network a threshold of Pearson’s |r|≥ 0.41 was estimated. For both mice networks the threshold was calculated with Pearson’s |r|≥ 0.84. Conclusions: Our analysis corrected the previously stated Pearson’s correlation coefficient threshold from 0.4 to 0.41 in the water recovery network and from 0.4 to 0.23 for the normal irrigation network. Furthermore, the proposed method suggested a correlation threshold of 0.84 for both mice networks rather than a threshold of 0.7 as applied earlier. We demonstrate that the proposed approach is a valuable tool for constructing biological meaningful networks.
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    ACS-Net: A Deep Unfolded ADMM Framework for Ultrasound Attenuation Imaging
    (IEEE Computer Society, 2025)
    Ultrasound attenuation imaging is gaining traction for its promising clinical diagnostic applications. Estimation methods such as Spatially Weighted Fidelity and Regularization Terms (SWIFT) and its deep learning-aided variant (DL-SWIFT) have demonstrated improved contrast-to-noise ratio (CNR) and more consistent attenuation coefficient slope (ACS) estimates through the use of spatially weighted formulations. However, both methods may still produce artifacts in heterogeneous regions with abrupt backscatter coefficient changes. To address these limitations, we propose ACS-Net, a deep unfolded Alternating Direction Method of Multipliers framework that integrates learned denoising operations within the classical iterative optimization process to reduce ACS estimation bias while preserving inclusion delineation. Phantom experiments confirmed a bias reduction of more than 40% on inclusions, while in vivo thyroid nodule results showed that ACS-Net decreases background coefficient of variation by nearly 50% and improves CNR by more than 30% compared to SWIFT and DL-SWIFT. These findings highlight the clinical promise of deep unfolded optimization methods for reliable and accurate ultrasound attenuation imaging.
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