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
Resource Information
The item Uncovering morphoproteomic relationships using probabilistic graphical models and resource description framework knowledgebases represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri-St. Louis Libraries.This item is available to borrow from all library branches.
Resource Information
The item Uncovering morphoproteomic relationships using probabilistic graphical models and resource description framework knowledgebases represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri-St. Louis Libraries.
This item is available to borrow from all library branches.
- 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
- Language
- eng
- Extent
- 1 online resource (viii, 101 pages)
- Note
- Advisor: Chi-Ren Shyu
- 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
- 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
- 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
- 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
Library Locations
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St. Louis Mercantile LibraryBorrow it1 University Blvd, St. Louis, MO, 63121, US38.710138 -90.311107
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University ArchivesBorrow it703 Lewis Hall, Columbia, MO, 65211, US
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University of Missouri-St. Louis Libraries DepositoryBorrow it2908 Lemone Blvd, Columbia, MO, 65201, US38.919360 -92.291620
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University of Missouri-St. Louis Libraries DepositoryBorrow it2908 Lemone Blvd, Columbia, MO, 65201, US38.919360 -92.291620
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Ward E Barnes Education LibraryBorrow it8001 Natural Bridge Rd, St. Louis, MO, 63121, US38.707079 -90.311355
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.umsl.edu/portal/Uncovering-morphoproteomic-relationships-using/VAyTvGZCXjU/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.umsl.edu/portal/Uncovering-morphoproteomic-relationships-using/VAyTvGZCXjU/">Uncovering morphoproteomic relationships using probabilistic graphical models and resource description framework knowledgebases</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.umsl.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.umsl.edu/">University of Missouri-St. Louis Libraries</a></span></span></span></span></div>
