Browsing by Author "Gullapelli, Rakesh"

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    Assessment of Parkinson's Disease Progression by Feature Relevance Analysis and Regression Techniques Using Machine Learning Algorithms
    Gullapelli, Rakesh; Jones, Josette; Lai, Patrick T. S.
    Remote patient tracking has been gaining increased attention due to its low-cost non-invasive methods. Unified Parkinson's Disease Rating Scale (UPDRS) is used often to track Parkinson's Disease (PD) symptoms which requires the patient's visit to the clinic and time consuming medical tests that may not be feasible for most of the elderly PD patients. One of the major concerns to predict the PD in early stages is that PD symptoms overlap with the symptoms of other diseases such as Multiple Sclerosis, Alzheimer's disease. Moreover, most of the current methods used for tracking PD rely on expert clinical raters, from which PD symptoms assessment may be difficult due to inter-individual variability. Predicting relevant features using machine learning algorithms is helpful in providing the scientific decision-making classification rules necessary to assess the disease progression in early stages.
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    Translational Systems Pharmacology Studies in Pregnant Women
    (Wiley, 2018-02) Quinney, Sara K.; Gullapelli, Rakesh; Haas, David M.; Obstetrics and Gynecology, School of Medicine
    Pregnancy involves rapid physiological adaptation and complex interplay between mother and fetus. New analytic technologies provide large amounts of genomic, proteomic, and metabolomics data. The integration of these data through bioinformatics, statistical, and systems pharmacology techniques can improve our understanding of the mechanisms of normal maternal physiologic changes and fetal development. New insights into the mechanisms of pregnancy-related disorders, such as preterm birth (PTB), may lead to the development of new therapeutic interventions and novel biomarkers.