Browsing by Author "Shen, L."

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    Genome-wide association with MRI atrophy measures as a quantitative trait locus for Alzheimer's disease
    (Nature Publishing Group, 2011-11) Furney, SJ; Simmons, A.; Breen, G.; Pedroso, I.; Lunnon, K.; Proitsi, P.; Hodges, A.; Powell, J.; Wahlund, L-O; Kloszewska, I.; Mecocci, P.; Soininen, H.; Tsolaki, M.; Vellas, B.; Spenger, C.; Lathrop, M.; Shen, L.; Kim, S.; Saykin, AJ; Weiner, MW; Lovestone, S.; Alzheimer's Disease Neuroimaging Initiative and the AddNeuroMed Consortium; Radiology and Imaging Sciences, School of Medicine
    Alzheimer's disease (AD) is a progressive neurodegenerative disorder with considerable evidence suggesting an initiation of disease in the entorhinal cortex and hippocampus and spreading thereafter to the rest of the brain. In this study, we combine genetics and imaging data obtained from the Alzheimer's Disease Neuroimaging Initiative and the AddNeuroMed study. To identify genetic susceptibility loci for AD, we conducted a genome-wide study of atrophy in regions associated with neurodegeneration in this condition. We identified one single-nucleotide polymorphism (SNP) with a disease-specific effect associated with entorhinal cortical volume in an intron of the ZNF292 gene (rs1925690; P-value=2.6 × 10(-8); corrected P-value for equivalent number of independent quantitative traits=7.7 × 10(-8)) and an intergenic SNP, flanking the ARPP-21 gene, with an overall effect on entorhinal cortical thickness (rs11129640; P-value=5.6 × 10(-8); corrected P-value=1.7 × 10(-7)). Gene-wide scoring also highlighted PICALM as the most significant gene associated with entorhinal cortical thickness (P-value=6.7 × 10(-6)).
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    Graphic Mining of High-Order Drug Interactions and Their Directional Effects on Myopathy Using Electronic Medical Records
    (Wiley, 2015-08) Du, L.; Chakraborty, A.; Chiang, C.-W.; Cheng, L.; Quinney, S.K.; Wu, H.; Zhang, P.; Li, L.; Shen, L.; Department of Medicine, IU School of Medicine
    We propose to study a novel pharmacovigilance problem for mining directional effects of high-order drug interactions on an adverse drug event (ADE). Our goal is to estimate each individual risk of adding a new drug to an existing drug combination. In this proof-of-concept study, we analyzed a large electronic medical records database and extracted myopathy-relevant case control drug co-occurrence data. We applied frequent itemset mining to discover frequent drug combinations within the extracted data, evaluated directional drug interactions related to these combinations, and identified directional drug interactions with large effect sizes. Furthermore, we developed a novel visualization method to organize multiple directional drug interaction effects depicted as a tree, to generate an intuitive graphical and visual representation of our data-mining results. This translational bioinformatics approach yields promising results, adds valuable and complementary information to the existing pharmacovigilance literature, and has the potential to impact clinical practice.