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Comparisons of statistical methods for determining gene expression signatures to predict binary cancer response

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TitleInfo
Title
Comparisons of statistical methods for determining gene expression signatures to predict binary cancer response
Name (type = personal)
NamePart (type = family)
Dong
NamePart (type = given)
Qian
NamePart (type = date)
1975-
DisplayForm
Qian Dong
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Moore
NamePart (type = given)
Dirk F
DisplayForm
Dirk F Moore
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Kim
NamePart (type = given)
Sinae
DisplayForm
Sinae Kim
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Martkert
NamePart (type = given)
Elke Katrin
DisplayForm
Elke Katrin Martkert
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = personal)
NamePart (type = family)
Wu
NamePart (type = given)
Chengqing
DisplayForm
Chengqing Wu
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Public Health
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2014
DateOther (qualifier = exact); (type = degree)
2014-10
CopyrightDate (encoding = w3cdtf)
2014
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
Cancer is a major public health problem with high mortality and mobility. In the past few decades, developments and progress of high-throughput molecular technologies have been used in diagnosing and managing treatments for cancers. Cancer classification using gene expression data poses many challenges to classical supervised learning methods. The main objective of this dissertation is to evaluate and compare the performances of six selected different classification methods, denoted as Logit (logistic regression), Lasso (least absolute shrinkage and selection operator), CART (classification and regression tree), RF (random forest), GBM (gradient boosted models), and SVM (support vector machine), for predicting binary cancer outcomes using gene expression data. We compare the performance using both real life datasets (prostate cancer data and breast cancer data) and extensive simulation experiments. Consistent with findings from previous comparisons of classifiers, the best classifier for predicting binary outcome varies with the dataset and the evaluation measures. No universally best performed classifier is identified which can work for all empirical datasets and under all simulation scenarios. When we compare different methods for classifications, especially classifiers for predicting cancer outcomes, accuracy should not be only thing we consider; other factors, such as simplicity to implement, ease of interpretation for clinicians or biologists, the biological insights that can be gained from the analysis results of a classifier, should also be taken into account. In addition, we have provided clear and easy-to-follow procedures of predictive model building and performance assessment for clinical researchers when there is a need to compare classification results from different classifier. We have addressed the binary classification problem in our thesis, but this approach should be easily applied to multi-category classification problems or to survival analysis problems. Based on results from real life datasets and extensive simulation experiments, we have found that when working with classification problem using high dimensional data, simple but widely used classification method, such as logistic regression has its limitation, and may not achieve the desirable performance. Classifiers designed to handle large numbers of predictors, such as Lasso, GBM, SVM and RF, are better choice in such situations.
Subject (authority = RUETD)
Topic
Public Health
Subject (authority = ETD-LCSH)
Topic
Statistical methods
Subject (authority = ETD-LCSH)
Topic
Gene expression--Statistical methods
Subject (authority = ETD-LCSH)
Topic
Cancer--Genetic aspects
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_5837
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xxvi, 234 p. : ill.)
Note (type = degree)
Dr.P.H.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
Qian Dong
RelatedItem (type = host)
TitleInfo
Title
School of Public Health ETD Collection
Identifier (type = local)
rucore10007500001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/T3V69M6G
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Dong
GivenName
Qian
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2014-09-15 14:22:55
AssociatedEntity
Name
Qian Dong
Role
Copyright holder
Affiliation
Rutgers University. School of Public Health
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.
RightsEvent
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2014-10-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2015-10-31
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after October 31st, 2015.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
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