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Prognosis of Dengue Hemorrhagic Fever Utilizing Human Genome Data and Machine Learning Technology

Toki, Autumn (2022) Prognosis of Dengue Hemorrhagic Fever Utilizing Human Genome Data and Machine Learning Technology. Master's Thesis, University of Pittsburgh. (Unpublished)

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Dengue is of worldwide public health concern as it has become one of the most significant arthropod-borne diseases, affecting an estimated 4 billion individuals globally. Dengue infections range from asymptomatic, to the debilitating febrile illness dengue fever (DF), and to dengue hemorrhagic fever (DHF), which is severe and potentially life-threatening. The precise immunopathology of DENV infection has yet to be elucidated, however, it has been shown that host genetics influence infection and disease development. Single nucleotide polymorphisms (SNPs) offer early clinical identification and better understanding of disease progression. Our study utilizes a cohort of patients from Brazil, who are infected with Dengue, and who have developed either DF or DHF. Individuals were genotyped for 20 immune-relevant SNPs, including a core set of 13 SNPs recognized to be most influential in disease progression. A machine learning (ML) algorithm together with human genotyped data was utilized to identify the combination of SNPs that have the greatest utility in predicting risk for developing the severe dengue phenotype. From this study, dengue severity can be predicted based solely on human genomic markers.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Toki, Autumnaut11@pitt.eduaut11
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairMartinson, Jeremyjmartins@pitt.edujmartins
Committee MemberMarques, Ernestomarques@pitt.edumarques
Committee MemberDemirci, Yesimfyd1@pitt.edufyd1
Date: 12 May 2022
Date Type: Publication
Defense Date: 21 April 2022
Approval Date: 12 May 2022
Submission Date: 29 April 2022
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 60
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Infectious Diseases and Microbiology
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Dengue Hemorrhagic Fever, SNPs, Advanced Neural Network
Date Deposited: 12 May 2022 14:46
Last Modified: 12 May 2022 14:46


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