The Resource Uncovering morphoproteomic relationships using probabilistic graphical models and resource description framework knowledgebases

Uncovering morphoproteomic relationships using probabilistic graphical models and resource description framework knowledgebases

Label
Uncovering morphoproteomic relationships using probabilistic graphical models and resource description framework knowledgebases
Title
Uncovering morphoproteomic relationships using probabilistic graphical models and resource description framework knowledgebases
Creator
Author
Subject
Language
eng
Summary
The heterogeneity of individual patient responses to conventional drug therapies is one of the central problems in personalized medicine and has great impact on clinical outcomes. To address this problem a new field of morphoproteomics was recently introduced. Morphoproteomics is a new method aimed at comprehensive analysis of protein circuitries in diseased cells to design effective drug therapies for individual patient cases. However, due to the overwhelming amount of molecular information that needs to be processed, successful adoption of morphoproteomics will greatly depend on availability of a comprehensive computerized knowledgebase and intelligent retrieval technologies. We have, therefore, initiated new research with the overall goal to develop informatics methods to support morphoproteomic studies. We integrate evidence and information extracted from Whole Slide Imaging (WSI) and Immunohistochemistry (IHC) as well as from a semantic "mashup" of publicly available knowledge sources to provide pathologists a comprehensive picture of morphoproteomic mechanisms. This dissertation introduces novel methods for improving IHC antibody/antigen test selection as well as uncovering morphoproteomic relationships using probabilistic graphical models and Resource Description Framework (RDF) graphs of biomedical knowledgebases. Our methods have great potential to bring a broad impact in to pathology and personalize medicine as well as to be extended to more general systems biology domain
Cataloging source
MUU
http://library.link/vocab/creatorName
Shin, Dmitriy
Degree
Ph. D.
Dissertation note
Thesis
Dissertation year
2012.
Government publication
government publication of a state province territory dependency etc
Granting institution
University of Missouri--Columbia,
Index
no index present
Literary form
non fiction
Nature of contents
dictionaries
Label
Uncovering morphoproteomic relationships using probabilistic graphical models and resource description framework knowledgebases
Instantiates
Publication
Contributor
Thesis advisor
Note
Advisor: Chi-Ren Shyu
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier.
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent.
Control code
872566729
Extent
1 online resource (viii, 101 pages)
Form of item
online
Media category
computer
Media MARC source
rdamedia.
Media type code
  • c
Specific material designation
remote
System control number
(OCoLC)872566729
Label
Uncovering morphoproteomic relationships using probabilistic graphical models and resource description framework knowledgebases
Publication
Contributor
Thesis advisor
Note
Advisor: Chi-Ren Shyu
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier.
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent.
Control code
872566729
Extent
1 online resource (viii, 101 pages)
Form of item
online
Media category
computer
Media MARC source
rdamedia.
Media type code
  • c
Specific material designation
remote
System control number
(OCoLC)872566729

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