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- W1567509752 abstract "With the increasing number of projects aiming to find the ultimate prognostic or diagnostic biomarker, many studies pursue only brief lists of biomarkers, dismissing information from groups of correlated biomarkers. Using a novel Bayesian Graphical Network (BGN) method, and data from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging, this study aimed to assess the connectivity between blood based proteins in discovering useful biomarkers. AIBL study participants were given the following clinical classifications: super healthy control (sHC), healthy control (HC), mild cognitive impairment (MCI) and Alzheimer's disease (AD). Briefly, three groups of protein markers (18, 37 & 49 proteins respectively) were assessed for the posterior probability of biological connection both within and between participants clinical classification. Using the smaller group of proteins, posterior probabilities between clinical classifications were very similar, indicating no difference in biological connections between groups. Increasing the number of proteins improved the ability to separate both sHC and HC from the AD group (0 for complete separation, 1 for complete similarity), with posterior probabilities ranging from 0.89 for the 18 protein group, through to 0.54 for the 37 protein group, and finally 0.28 for the 49 protein group. We identified Beta 2 Macroglobulin (B2M) as a master regulator of multiple proteins across all classifications, while relationships between key disease specific proteins such as Apolipoprotein E (ApoE) and Apolipoprotein H (ApoH) were not consistent between HC and AD participants. We suggest larger protein interaction networks provide more information about disease processes and may be useful for identification of therapeutic targets." @default.
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- W1567509752 date "2014-07-01" @default.
- W1567509752 modified "2023-10-06" @default.
- W1567509752 title "P4-039: BAYESIAN GRAPHICAL NETWORK ANALYSES REVEALS COMPLEX BIOLOGICAL INTERACTIONS SPECIFIC TO ALZHEIMER'S DISEASE" @default.
- W1567509752 doi "https://doi.org/10.1016/j.jalz.2014.05.1553" @default.
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