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Clinical research approval processes are complex since they involve human subject welfare as well as regulatory and ethical concerns. Typically, clinical research institutions have an elaborate established review process; the labor-intensive and time-consuming nature of this process can result in approval delays thus significantly impacting the biomedical(More)
Biased G protein-coupled receptor agonists engender a restricted repertoire of downstream events from their cognate receptors, permitting them to produce mixed agonist-antagonist effects in vivo. While this opens the possibility of novel therapeutics, it complicates rational drug design, since the in vivo response to a biased agonist cannot be reliably(More)
This paper describes potential strategies for data modeling and implementation as part of the general architecture of CSIS, a Clinical Study and Informatics System that has been developed for the National Institute of Neuroscience and Stroke (NINDS). We discuss the NINDS requirements and how they influenced the system design, with an emphasis on dynamic(More)
Longitudinal studies play a key role in various fields, including epidemiology, clinical research, and genomic analysis. Currently, the most popular methods in longitudinal data analysis are model-driven regression approaches, which impose strong prior assumptions and are unable to scale to large problems in the manner of machine learning algorithms. In(More)
Research into modeling the progression of Alzheimer's disease (AD) has made recent progress in identifying plasma proteomic biomarkers to identify the disease at the pre-clinical stage. In contrast with cerebral spinal fluid (CSF) biomarkers and PET imaging, plasma biomarker diagnoses have the advantage of being cost-effective and minimally invasive,(More)
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