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    A Multivariate Biomarker for Parkinson’s Disease
    (In the Proceedings of 12th Annual Research Day, 2014 - Pace University, 2015-05-15) Crocetti, Giancarlo; Coakley, Michael; Dressner, Phil; Kellum, Wanda; Lamin, Tamba
    In this study, we executed a genomic analysis with the objective of selecting a set of genes (possibly small) that would help in the detection and classification of samples from patients affected by Parkinson Disease. We performed a complete data analysis and during the exploratory phase, we selected a list of differentially expressed genes. Despite their association with the diseased state, we could not use them as a biomarker tool. Therefore, our research was extended to include a multivariate analysis approach resulting in the identification and selection of a group of 20 genes that showed a clear potential in detecting and correctly classify Parkinson Disease samples even in the presence of other neurodegenerative disorders.
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    Transforming telemedicine through big data analytics
    (In the Proceedings of 12th Annual Research Day, 2014 - Pace University, 2015-05-15) Coakley, Michael; Crocetti, Giancarlo; Dressner, Phil; Kellum, Wanda; Lamin, Tamba
    A look at how big data is transforming telemedicine to provide better care by tapping into a larger source of patient information. Telemedicine will have a profound impact on patient care, increase access and quality, and represent an opportunity to keep health care costs down. Data generated by smart devices will enable the real-time monitoring of chronic diseases, allowing optimal dosage of drugs and improve patient outcomes