RUcore: Rutgers University Community Repository
Biomarker discovery for microarray data by enriched methods, stochastic approximation and mixed effect models
Descriptive
TitleInfo
Title
Biomarker discovery for microarray data by enriched methods, stochastic approximation and mixed effect models
Name
(type = personal)
NamePart
(type = family)
Yi
NamePart
(type = given)
Lan
NamePart
(type = date)
1984-
DisplayForm
Lan Yi
Role
RoleTerm
(authority = RULIB)
author
Name
(type = personal)
NamePart
(type = family)
Cabrera
NamePart
(type = given)
Javier
DisplayForm
Javier Cabrera
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
chair
Name
(type = personal)
NamePart
(type = family)
Xiao
NamePart
(type = given)
Han
DisplayForm
Han Xiao
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Dicker
NamePart
(type = given)
Lee
DisplayForm
Lee Dicker
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Emir
NamePart
(type = given)
Birol
DisplayForm
Birol Emir
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
Graduate School - New Brunswick
Role
RoleTerm
(authority = RULIB)
school
TypeOfResource
Text
Genre
(authority = marcgt)
theses
OriginInfo
DateCreated
(qualifier = exact)
2014
DateOther
(qualifier = exact);
(type = degree)
2014-05
Place
PlaceTerm
(type = code)
xx
Language
LanguageTerm
(authority = ISO639-2b);
(type = code)
eng
Abstract
(type = abstract)
Nowadays microarray technology enables scientists to monitor the expression levels of hundreds of thousands of genes simultaneously. Because of the high cost of such experiments, the sample size is small, typically, only a few dozen. In this thesis, we propose a new perspective on microarray data. We believe microarray data generally contain three types of signals: specific signal, non-specific signal and spurious signal. We propose an enriched method for biomarker discovery which strengthens the specific signal (biomarkers) and weakens the spurious signal. We show that our enriched version of principal component analysis will highlight the specific signals in the data and can help separate different signals. We also show that enriched principal component analysis along with linear discriminant analysis will improve the classification and prediction of microarray data, comparing to some other popular methods. The results from our method are easy to interpret, too. We also prove the stochastic approximation procedure used in conditional t test converges under some general assumptions. Finally we discuss about analyzing the data from one novel experiment to find groups of genes (biomarkers), applying hierarchical clustering and nonlinear mixed effect models.
Subject
(authority = RUETD)
Topic
Statistics and Biostatistics
RelatedItem
(type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
(type = RULIB)
ETD
Identifier
ETD_5503
PhysicalDescription
Form
(authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
ix, 135 p. : ill.
Note
(type = degree)
Ph.D.
Note
(type = bibliography)
Includes bibliographical references
Note
(type = statement of responsibility)
by Lan Yi
Subject
(authority = ETD-LCSH)
Topic
DNA microarrays
Subject
(authority = ETD-LCSH)
Topic
Biochemical markers
RelatedItem
(type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier
(type = local)
rucore19991600001
Location
PhysicalLocation
(authority = marcorg);
(displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier
(type = doi)
doi:10.7282/T3TH8K17
Genre
(authority = ExL-Esploro)
ETD doctoral
Back to the top
Rights
RightsDeclaration
(ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder
(type = personal)
Name
FamilyName
Yi
GivenName
Lan
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
(encoding = w3cdtf);
(qualifier = exact);
(point = start)
2014-04-14 16:14:04
AssociatedEntity
Name
Lan Yi
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
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
Back to the top
Statistical Profile