Staff View
Using importance sampling to improve accuracy and repeatability of CEESIt

Descriptive

TitleInfo
Title
Using importance sampling to improve accuracy and repeatability of CEESIt
Name (type = personal)
NamePart (type = family)
Hui
NamePart (type = given)
Selina
DisplayForm
Selina Hui
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Lun
NamePart (type = given)
Desmond S
DisplayForm
Desmond S Lun
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Camden Graduate School
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (encoding = w3cdtf); (qualifier = exact)
2019
DateOther (encoding = w3cdtf); (qualifier = exact); (type = degree)
2019-10
Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract (type = abstract)
CEESIt is a computational method for the analysis of short tandem repeats (STRs) in DNA for human identification. CEESIt computes the likelihood ratio (LR), the ratio of the probability of the evidence (the electropherogram obtained from the DNA sample) given a specific person of interest (POI) to the probability of the evidence given a random contributor from the background population. The DNA sample may be a mixture, comprised of multiple contributors at different ratios. With cases using low amounts of template DNA or cases with multiple contributors in the mixture, the results lacked consistency between computations. With 1-person mixtures, the tests ran with high repeatability and short runtime but with 2-people mixtures, the results had varying results and significantly longer runtime. The goal was to find the source of the discrepancies to improve repeatability and accuracy. CEESIt uses the Monte Carlo Method to generate the final probabilistic values. To improve repeatability and accuracy, importance sampling of the genotypes of the background population was implemented. By careful sampling and appropriate weighting to represent the background population, this improved the overall accuracy in the algorithm and allowed the algorithm to sample a smaller population, which decreases runtime.
Subject (authority = RUETD)
Topic
Computer Science
Subject (authority = LCSH)
Topic
DNA fingerprinting
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_10245
PhysicalDescription
Form (authority = gmd)
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (ii, 18 pages) : illustrations
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
RelatedItem (type = host)
TitleInfo
Title
Camden Graduate School Electronic Theses and Dissertations
Identifier (type = local)
rucore10005600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/t3-9h3h-h084
Genre (authority = ExL-Esploro)
ETD graduate
Back to the top

Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Hui
GivenName
Selina
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-09-19 10:38:40
AssociatedEntity
Name
Selina Hui
Role
Copyright holder
Affiliation
Rutgers University. Camden Graduate School
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
Back to the top

Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
CreatingApplication
Version
1.4
ApplicationName
Mac OS X 10.13.6 Quartz PDFContext
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2019-09-20T18:11:33
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2019-09-20T18:11:33
Back to the top
Version 8.3.13
Rutgers University Libraries - Copyright ©2021