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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Bayesian model selection for pathological data.

Carole H Sudre, Manuel Jorge Cardoso, Willem Bouvy

    Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
    |October 22, 2014
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    Summary
    This summary is machine-generated.

    This study introduces an unsupervised framework to automatically classify brain image patterns associated with various pathologies. This method aids in understanding brain data and identifying disease biomarkers without prior diagnostic information.

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    Area of Science:

    • Neuroimaging
    • Computational Biology
    • Medical Image Analysis

    Background:

    • Accurate detection of abnormal brain image intensities is crucial for understanding pathologies.
    • Model selection for pathological neuroimaging data impacts biological plausibility and biomarker extraction.
    • Current methods often require prior knowledge of pathological status, limiting unbiased analysis.

    Purpose of the Study:

    • To develop a fully unsupervised hierarchical model selection framework for neuroimaging data.
    • To enable the stratification of different types of abnormal image patterns.
    • To facilitate a better understanding of underlying data and biological processes without prior pathological information.

    Main Methods:

    • Implementation of a hierarchical model selection framework.
    • Application of unsupervised learning techniques to neuroimaging data.
    • Stratification of image patterns based on intensity abnormalities.

    Main Results:

    • The proposed framework successfully stratifies abnormal brain image patterns.
    • Demonstrated ability to categorize different pathological presentations without prior labels.
    • Provides a foundation for unbiased model fitting and biomarker discovery.

    Conclusions:

    • An unsupervised hierarchical model selection framework is effective for neuroimaging.
    • This approach allows for the identification of diverse pathological patterns.
    • Enables improved understanding of brain data and extraction of meaningful biomarkers.