TY - JOUR
T1 - Statistical contributions to proteomic research.
AU - Morris, Jeffrey S.
AU - Baggerly, Keith A.
AU - Gutstein, Howard B.
AU - Coombes, Kevin R.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
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U2 - 10.1007/978-1-60761-711-2_9
DO - 10.1007/978-1-60761-711-2_9
M3 - Article
C2 - 20407946
AN - SCOPUS:77955485761
SN - 1064-3745
VL - 641
SP - 143
EP - 166
JO - Methods in molecular biology (Clifton, N.J.)
JF - Methods in molecular biology (Clifton, N.J.)
ER -