Linked Life Data

20407946

Source:http://linkedlifedata.com/resource/pubmed/id/20407946

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2010-4-21
pubmed:abstractText
Proteomic profiling has the potential to impact the diagnosis, prognosis, and treatment of various diseases. A number of different proteomic technologies are available that allow us to look at many proteins at once, and all of them yield complex data that raise significant quantitative challenges. Inadequate attention to these quantitative issues can prevent these studies from achieving their desired goals, and can even lead to invalid results. In this chapter, we describe various ways the involvement of statisticians or other quantitative scientists in the study team can contribute to the success of proteomic research, and we outline some of the key statistical principles that should guide the experimental design and analysis of such studies.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1940-6029
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
641
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
143-66
pubmed:dateRevised
2011-1-4
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Statistical contributions to proteomic research.
pubmed:affiliation
Department of Biostatistics, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA.
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural