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amino acids can be discerned by the mass spec-
trometer and the relative amount of a given
protein that originated in the
has the potential to signi
cantly improve the
quality of these data sets. These advantages
have the potential to revolutionize the applica-
bility of the protein
e
protein interaction data,
making them a vital part of any effort to classify
biological
labeled
sample can be determined. Those proteins that
preferentially precipitate along with the protein
of interest from the heavy cell conditions are
selected for subsequent validation, but all
proteins found in equal amount are considered
nonspeci
“
heavy
”
finding relevant bio-
markers that will help in managing many of
today
stages and
'
s most lethal diseases.
c background proteins. Label-free
quantitation has also been used in an attempt
to
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proteomic analysis of eleven common cell lines reveals
ubiquitous but varying expression of most proteins.
Mol Cell Proteomics
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8.
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7.
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e
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identify
potential
nonspeci
c
binding
proteins. Here the two af
cations are
kept separate throughout the whole experi-
mental procedure. Following MS analysis, the
relative quantity of each protein is accessed by
estimating the accumulative intensity of all
peptides identi
nity puri
ed for each of the proteins.
Proteins with an intensity ratio of 1:1 are again
considered background proteins, but the ones
for which the intensity is signi
cantly higher
for the af
nity pulldown performed with the
protein of
interest are considered potential
c interacting partners.
71,72
speci
CONCLUSION
Over the last several years, several groups have
started to utilize protein
e
protein interaction data
in conjunction with more classical data sets, such
as expression array, to classify biological systems
with the aim of identifying highly relevant
biomarkers that can be used in disease detection,
management, and drug treatment. These efforts
have highlighted both the advantages and the
shortcoming of using these data sets.
The relatively high false positive and negative
discovery rates in the methods presently used to
identify protein
e
protein interactions are the
biggest challenge in using the available pro-
tein
e
protein interaction data. However,
cation of dysfunctional
modules and disease genes in congenital heart
disease by a network-based approach.
BMC Genomics
2011;
12
:592.
12. Azuaje F, Devaux Y, Wagner DR. Coordinated modular
functionality and prognostic potential of a heart failure
biomarker-driven interaction network.
BMC Syst Biol
2010;
4
:60.
13. Azuaje F, Devaux Y, Vausort M, et al. Transcriptional
networks characterize ventricular dysfunction after
the
recognition and active search for highly ef
cient
methods to identify background proteins in
conjunction with improved curation methods
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