Biomedical Engineering Reference
In-Depth Information
From the number of cliques or seeds of the communities are obtained
and to get the
nal community the seed has to expand until all the
satisfactory nodes are included. For the
nal community judgment set a
threshold parameter T
d
, and utilizing it to decide which node is to be
added into it [
11
14
].
-
Finally, the algorithm shows that the CACE can detect the functional modules
much more effectively and accurately when compared with other algorithms.
2.2.4 PE-weighted Clustering Coef
cient (PE-WCC)
In this algorithm, it
rst assesses the reliability of the protein interaction data using
the PE measure and then predicts the protein complexes based on the concept of
weighted clustering coef
cient. It detects the more matched complexes with higher
quality scores.
Step: (1) For assessing the reliability of the protein interaction data, we introduce
the value called as PE measure where it reduces the noise level asso-
ciated with the PPI networks
Y
v
l
ð
p
ð
ij
¼
1
1
ð
p
k
1
Þ
il
ð
p
k
1
Þ
jl
Þ
ð
5
Þ
where the product by all v
l
:
ð
v
l
;
v
i
Þ2
E
; ð
v
l
;
v
i
Þ2
E then for each protein in the PPI
network, we calculate the average PE measures.
P
V
l
p
il
N
i
i
¼
W
avg
ð
6
Þ
where v
l
:
ð
v
l
;
v
i
Þ2
E, N
i
is the number of the neighbors of v
i
and i =1,
…
, N. If the
PE measure p
il
is less than the average (w
avg
)
i
, then edge between proteins i and l is
considered unreliable and therefore, it should be removed from the network.
Step: (2) For detecting protein complex using weighted clustering coef
cient for
each protein v
i
in the PPI network, we
rst create the neighborhood
graph, calculate the weighted clustering coef
cient, and then calculate
the degree of each node in the neighborhood graph; the
of a
node being the number of its neighbors. The weighted clustering coef-
“
degree
”
cient c
i
in this case is calculated according to the following formula:
2
N
3cliques
c
i
¼
ð
7
Þ
N
i
ð
N
i
1
Þ
Search WWH ::
Custom Search