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Lei et al. BMC Genomics 2015, 16(Suppl 3):S3
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PROCEEDINGS
Open Access
Clustering PPI data by combining FA and SHC
method
Xiujuan Lei1,2, Chao Ying1, Fang-Xiang Wu3*, Jin Xu2
From 10th International Symposium on Bioinformatics Research and Applications (ISBRA-14)
Zhangjiajie, China. 28-30 June 2014
Abstract
Clustering is one of main methods to identify functional modules from protein-protein interaction (PPI) data.
Nevertheless traditional clustering methods may not be effective for clustering PPI data. In this paper, we proposed
a novel method for clustering PPI data by combining firefly algorithm (FA) and synchronization-based hierarchical
clustering (SHC) algorithm. Firstly, the PPI data are preprocessed via spectral clustering (SC) which transforms the
high-dimensional similarity matrix into a low dimension matrix. Then the SHC algorithm is used to perform
clustering. In SHC algorithm, hierarchical clustering is achieved by enlarging the neighborhood radius of
synchronized objects continuously, while the hierarchical search is very difficult to find the optimal neighborhood
radius of synchronization and the efficiency is not high. So we adopt the firefly algorithm to determine the optimal
threshold of the neighborhood radius of synchronization automatically. The proposed algorithm is tested on the
MIPS PPI dataset. The results show that our proposed algorithm is better than the traditional algorithms in
precision, recall and f-measure value.
Introduction
Protein-protein interaction(PPI) data [1] have been very
important sources in the researches of life science, which
can explore biological functions so as to deeply understand the essence of life activities and mechanism of diseases. Clustering analysis of PPI data is an effective way to
predict the function modules and protein complex and,
study mechanisms, diagnosis and treatment of diseases.
PPI data are often represented as PPI network. Traditional clustering methods do not perform well for PPI data
due to the properties of their represented networks such
as small world and scale free characters [1,2]. Many new
algorithms were proposed for clustering PPI networks
[3,4]. In 2002 years, Girvan and Newman[5] proposed a
clustering algorithm based on hierarchical divisions, which
deletes the edge with the biggest betweenness [6,7] constantly to separate modules. The Newman fast algorithm
[8] is a kind of clustering algorithm based on hierarchy
condensations, in which the algorithm continually merges
* Correspondence: faw341@mail.usask.ca
3
Division of Biomedical Engineering, University of Saskatchewan, Saskatoon,
SK S7N 5A9, Canada
Full list of author information is available at the end of the article
two modules that have the highest similarity. Restricted
Neighborhood Search Clustering (RNSC) algorithm [9] is
another kind of clustering algorithm based on graph partitioning, which starts with a random partition of a network
and iteratively moves the nodes on the border of a cluster
into the adjacent cluster to search for a better clustering
result with the minimum cost. Clique Percolation Method
(CPM) was put forward by Palla [10], in which the kcliques was identified by using clique percolation firstly,
and then the adjacent k-cliques were combined to get the
functional modules. Bader et al. proposed molecular complex detection (MCODE) [11], in which every node was
weighted by the node’s local neighbor density firstly, then
the nodes with high weights were picked as the seed nodes
of initial clusters and further these clusters were augmented to form the preliminary clusters. Markov clustering
(MCL) [12] is a graph clustering based on flow simulation,
which has been applied to detect functional modules
through simulating random walks within a graph. Spectral
clustering-based (SC) method [13] converts the problem
to a quadratic optimization with constraints by utilizing
the methodology of matrix analysis, which is generally
applied to the fields of image segmentation and complex
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network clustering. Some methods advise that we should
consider the gene expression data and detect protein complexes basing on uncertain graph model [14,15],There are
many new algorithms also, such as Ovrlp, PE-WCC,
UVCluster, AP, GFA, ADMSC, SCI-BN, CORE, FAG-EC,
HC-PIN, IPCA, CP-DR, LF-PIN, ABC algorithm [16-29]
and so on.
Synchronization is a natural phenomenon ranging from
the metabolism in the cell to social behavior in groups of
individuals regulating a large variety of complex processes. The sync [30] algorithm inherited from synchronization, which is a novel approach to cluster objects
inspired by the powerful concept of synchronization. The
basic idea is to regard each object as a phase oscillator
and simulate their interaction behaviors over time. The
similar phase oscillators synchronize together and form
distinct clusters naturally along with time increasing.
Without depending on any distribution assumptions, the
sync algorithm can detect clusters of arbitrary number,
shape and size. In addition, because the outliers do not
synchronize with cluster objects, the concept of synchronization allows handling the natural outliers. However,
the running time of the algorithm is too long to process
the large-scale data. The running time of the algorithm
consists of two parts primarily: the dynamic interaction
time of synchronizing objects and the process of determining the optimal synchronous neighborhood radius.
For reducing the dynamic interaction time of synchronization of data, the concept of ε-neighborhood closures
was proposed in the synchronization-based hierarchical
clustering (SHC) [31,32] algorithm, the objects in a
neighborhood closures will reach synchronization completely and eventually form a cluster. So it can detect
clusters by putting the objects in the same neighborhood
closures to a cluster even if the objects do not synchronize completely. However, the SHC algorithm determines
the optimal value of synchronous neighborhood radius by
means of hierarchical search that the sync algorithm
does. The hierarchical search for the optimal value of
synchronous neighborhood radius not only has low efficiency but also has other two shortcomings. The hierarchical search is very difficult to find the optimal value
of synchronous neighborhood radius, and the hierarchical incremental Δε needs to be adjusted according to the
different object distributions.
Swarm intelligence optimization algorithm is a kind of
bionic algorithms developed in recent years, which is
characterized by simply handling, collateral implementation and strong robustness. The searching process for
the optimal value of swarm intelligence optimization
does not require the solution set differentiable or even
continuous. So the swarm intelligence optimization
algorithm is applied extensively to pattern recognition,
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automatic control, robot path planning and other fields.
The firefly algorithm (FA) [33-35] is an intelligent optimization algorithm developed by simulating the glowing
characteristics of fireflies based on group searching. The
bionic principle of the FA algorithm is looking for partners in the searching area according to the glowing
characteristics of fireflies, and then moving towards the
brighter firefly. Regarding points in the solution set as
fireflies, the searching process in solution space is
viewed as attraction and movements of fireflies. After
many times of movements, all individuals will be gathered in the position with the highest brightness of fireflies, so as to achieve optimization. The process of
optimization of the firefly intelligent algorithm is simple
and efficient, and therefore is widely applied to functional optimization and combinatorial optimization.
Combining the advantages of the SHC algorithm and
the optimization ability of the FA algorithm noted
above, it is naturally to adopt the FA to improve the
SHC algorithm. Using the FA algorithm to find the optimal value of synchronous neighborhood radius will be
more efficient and accurate than the basic hierarchical
search do. In addition it is applicable to arbitrary data
distribution.
The paper is organized as follows: in Section “Materials
and method”, basic concepts and principles are introduced firstly; secondly the proposed model of clustering
is discussed, and then the flow chart is listed, along with
the time complexity analysis of the algorithm. Performance and evaluation of the proposed algorithm is
shown by comparing with SC and SHC in Section
“Results and Discussions”. The last Section concludes
this research.
Materials and method
The SHC algorithm
The phenomenon of synchronization often appears in
physics, it can be expressed as follows. Two or more
dynamic systems both have their own evolution and
mutual coupling. This effect can be either one-way or
two-way streets. When meets certain conditions, the
output of these systems will eventually converge and
completely be equal under the influence of coupling,
this process is called synchronization. The Kuramotom
model [36,37] is applied widely as the simple model of
synchronization behavior, the generalized definition of
Kuramotom model is shown as follows:
Definition 1 (Generalize Kuramoto model): The Kuramoto model consists of a population of N coupled phase
oscillators θi(t) whose dynamics are governed by:
θi = ωi +
N
j=1
Kij sin(θj − θi )
(1)
Lei et al. BMC Genomics 2015, 16(Suppl 3):S3
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where ωi is its natural frequencies and is distributed
with a given probability density g(ω).
Each oscillator tries to run independently at its own
frequency, while the coupling tends to synchronize it to
all the others.
The sync algorithm is a novel approach for clustering
inspired by the powerful concept of synchronization. It
regards each data object as a phase oscillator, and each
dimension coordinates corresponding to a phase value of
the oscillator. Each object couples with data objects in its
ε-neighborhood, where ε is the neighborhood radius. In
the effect of synchronization coupling, the object’s coordinates are transformed constantly, and objects with the
same coordinates will be classified eventually to the same
cluster, namely synchronization completion. Let x ∈ Rd
represents an object in the dataset X and xi be the i-th
dimension of the object x. The transformation formula of
coordinate of x shows as follows.
1
xi (t + 1) = xi (t) + sin(yi (t) − xi (t)) (2)
Nε (x(t)) y∈N (x(t))
ε
where ε-neighborhood is defined in Definition 2
below.
Definition 2 (ε-neighborhood): The ε-neighborhood
radius of an object is a collection of data with distances
to the object less than ε:
Nε (x) = y ∈ X|dist x, y ≤ ε
(3)
where dist(x,y) is the metric function of distance and the
Euclidean distance is often used. If the object y ∈ Nε(x), y
is called the ε-neighborhood of x, denoted by x ®ε y. The
relationship of ε-neighborhood between objects is symmetrical, namely if x ®ε y then y ®ε x.
For reducing the dynamic interaction time of synchronization of data in the sync algorithm, the concept of neighborhood closures is proposed in SHC algorithm. Objects
in a ε-neighborhood closure will reach synchronization
complete eventually. So it can detect the clusters even if
the objects have not yet reached the same coordinates by
classifying data in the same neighborhood closures to the
same cluster, which reduces the dynamic interaction time
of data.
Definition 3 (ε-neighborhood closures): Suppose objects
set X’ ⊆ X, in the dynamic process of synchronous clustering, if ∀x, y ∈ X’ satisfies x ®ε y, and if ∀x ∈ X, x ®ε z,
then z ∈ X’, X’ is called an ε-neighborhood closure, that is,
for any object x ∈ X’, Nε(x) = X’ is established.
a1, a2, a3, a4 form a ε-neighborhood closure in the Figure 1,
and will reach complete synchronization eventually.
The optimal value of synchronous neighborhood
radius needs to be determined in both the sync algorithm and the SHC algorithm. The SHC algorithm
determines synchronous neighborhood radius by means
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Figure 1 ε-neighborhood closures.
of the hierarchical search that the sync algorithm does.
The process of hierarchical search for the optimization
of the neighborhood radius shows as follows. Starting in
a small neighborhood radius value ε, then adding an
increment (marked as Δε) to ε at a time (ε = ε + Δε)
until the neighborhood radius is large enough to contain
all objects. Clustering in each neighborhood radius of ε,
and it is considered to be optimal when the ε gets the
best result of clustering.
The FA
The FA is a random optimization algorithm constructed
by simulating the group behavior of the fireflies. There
are two important elements in the FA, the light intensity
and the attractiveness. The former reflects the advantages and disadvantages of locations of fireflies and the
latter determines the movement distances of fireflies
attracted. The optimization process of the algorithm is
implemented through updating the light intensity and
the attractiveness constantly. The mathematical mechanism of the FA is described as follows.
The relative value of the light intensity of fireflies is
expressed as:
I = I0 × e−γ rij
(4)
where I0 is the initial light intensity (r = 0) related to
the objective function value, the higher the value of
objective function is, the stronger the initial light intensity I0 will be. g is the light absorption coefficient set to
reflect the features that the light intensity decreases gradually along with the increase of the distance and the
absorption of the medium. It can be set to a constant.
rij is the space distance between firefly i and firefly j.
The attractiveness of firefly is expressed as:
β = β0 × e−γ rij
2
(5)
where b0 is the maximum of attractiveness. g and rij
are the same as above.
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If firefly i moves to firefly j, the updating of location of
firefly i is expressed as:
xi (t + 1) = xi (t) + β × xj (t) − xi (t) + α × rand − 1/2 (6)
where xi(t), xj(t) are the space coordinates of firefly i
and firefly j at the time t, a is step-size in [0, 1], rand is a
random factor that follows uniform distribution in [0, 1].
Fireflies are distributed to the solution space randomly
first of all. Each firefly has its own light intensity according to its location, the light intensity is calculated according to Eq. (4). The firefly with low light intensity is
attracted by and moving to the firefly with higher light
intensity. The movement distance depends on the attractiveness between them calculated by Eq. (5). The location
updating of the fireflies is cumulated based on Eq. (6).
There is a disturbing term in the process of updating the
location, which enlarges the search area and avoids the
algorithm to fall into the local optimum too early. Finally
all fireflies will gather in the location of the maximum
light intensity.
The proposed clustering algorithm
The sync algorithm clusters objects based on the principle of dynamic synchronization, which has many advantages in that it reflects the intrinsic structure of the
dataset. For example, it can detect clusters of arbitrary
number, shape and size and not depend on any assumption of distribution. In addition, it can handle outliers
since the noise will not synchronize to cluster objects.
However, the running time of the algorithm consists of
two parts primarily: The dynamic interaction time of synchronization of data and the process of determining the
optimal value of synchronous neighborhood radius,
which is too long to process large-scale data.
Aiming to reduce the dynamic interaction time of the
sync algorithm, the concept of ε-neighborhood closures is
proposed in the SHC algorithm. It classifies objects in the
same neighborhood closures to a cluster even if objects
have not yet reached the same coordinate, which enhances
the efficiency of the algorithm by reducing the time of
dynamic interaction of data. However, the SHC algorithm
determines synchronous neighborhood radius by means of
hierarchical search that the sync algorithm does. The hierarchical search for synchronous neighborhood radius not
only has low efficiency but also has two shortcomings.
Firstly, the hierarchical search is very difficult to find the
optimal value of synchronous neighborhood radius in a
fixed increment. Secondly, the increment Δε needs to be
adjusted according to different data distributions. For
example, in the SHC algorithm, the initial value of ε is set
to the average distance of all objects of its three nearest
neighbors. The increment Δε is the different value of the
average distance of all objects to its four nearest neighbors
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minus the average distance of all objects to its three nearest neighbors. So the running time of the SHC algorithm
is very huge when the dataset is uniform and dispersive. In
addition, we must set Δε small when the data distribution
is approximate, otherwise it is hard to find the optimal
value of synchronous neighborhood radius.
The FA is a swarm intelligent optimization algorithm
developed by simulating the glowing characteristics of fireflies, which is speedy and precise in the optimization process. Using the firefly algorithm to search for the optimal
neighborhood radius of synchronous can overcome the
drawbacks of the hierarchical search. It adopts fewer
searching steps for the optimal value of synchronous
neighborhood radius and gets more accurate results than
the hierarchical search due to its intelligent searching strategies. So it saves time on determining the optimal value
of synchronous neighborhood radius. In addition, it is
applicable to any data distributions. So we improve the
SHC algorithm by means of the FA and apply the proposed algorithm to clustering PPI data.
Preprocessing of PPI data
The PPI data is expressed as a graph, called PPI network,
in which each node represents a protein and the edge
between two nodes represent the interaction between proteins. In that way, we get an n*n adjacency matrix of
nodes. However, the dimension of the adjacency matrix is
too big to deal with. Inspired by the spectral clustering, we
use the following way to reduce the dimension of the adjacency matrix of PPI.
First, a similarity matrix A of nodes is constructed as
follow.
⎧
⎪
k∈Iij w(i, k) ·
k∈I w(j, k)
⎨ |Ni ∩ Nj | + 1
ij
η
+ (1 − η) i = j
Aij =
min(Ni , Nj )
w(i, s) · t∈Ij w(j, t)
s∈N
i
⎪
⎩
0,
i=j
(7)
where N i , N j are neighbor nodes of nodes u and v
respectively. Iij is the common neighbors of i and j, w(i,j)
is the weight between i and j to measure the interaction
strength, and h is constant between 0 and 1.
Eq. (7) considers two aspects of the aggregation coefficient of edges and the weighted aggregation coefficient of
edges [38-40]. The first half of Eq. (7) is the aggregation
coefficient of edges based on degree, which is portrayed by
means of the ratio between adding 1 to the number of
common neighbors of two protein nodes and minimal
value of the number of neighbors of two nodes. The second half of Eq. (7) is the weighted aggregation coefficient
of edges, which is illustrated by the ratio between the product of summation of weight values of edges respectively
connecting these two nodes (i, j) with their common
neighbors (k) and the product of summation of weight
values of edges linking these two nodes (i, j) with their
corresponding neighbors (s, t). In addition, we use h to
balance the weight of the two parts.
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Then constructing Laplacian matrix L of matrix A, the
D is the diagonal matrix in which (i, i)-element is the
sum of A’s i-th row.
⎧
0
Dii = 0|Djj = 0
⎨
Lij = Aij
(8)
else
⎩
Dii Djj
Matrix X consists of eigenvectors of matrix L’s corresponding to the first three eigenvalues and X is normalized. X is an n*3 matrix, in which lines represent the
protein objects (corresponding to the protein nodes in PPI
network) and columns are the three-dimensional space
coordinates of the protein objects. Our proposed clustering algorithm is calculated based on X.
Design of solution space
The solution space of the position of the firefly corresponds to the neighborhood radius of synchronization.
The initial light intensity I0 of one firefly is assigned by
the calculation result of objective function, see Eq.(9),
which is expressed as the evaluation of clustering results
based on the neighborhood radius of the firefly. Moving
to the firefly with higher light intensity is regarded as to
search for the optimal value of synchronous neighborhood radius. The position of the firefly with the highest
light intensity means the optimal value of synchronous
neighborhood radius.
Definition of objective function
We choose the following object function to evaluate the
clustering results. Clusters with higher value of the
objective function mean the stronger modularity of clusters, namely, a better clustering result.
fval =
⎧
⎛
x ⎨
ρ
·⎝
2 · mHi / nHi + mHi
i=1 ⎩
u,v∈Hi ,w(u,v)∈W
w(u, v)/
u,v∈Hi ,w(v,k)∈W
⎞1−ρ ⎫
⎬
w (v, k)⎠
⎭
(9)
Where mH is the number of edges that connect points
in the cluster Hi , nH is the number of edges that connect
points in the cluster Hi with points out of the cluster Hi,
w(u,v) is the weight between point u and point v, x is the
number of clusters, W is the set of connections.
The first half of the Eq. (9) is the summation of the ratio
of its in-degree to the sum of its in-degree and its outdegree, the second part is the summation of the ratio of its
weighted in-degree to the sum of its weighted in-degree
and its weighted out-degree. The two parts calculate modularity respectively. We can change the proportion of two
parts by adjusting the parameter r.
Matrix X consist of the matrix L’s eigenvector that the
top three eigenvalues corresponded. X is an n*3 matrix,
in which the rows represent protein objects and the columns are the three-dimensional space coordinates of
protein objects.
Step 2 The setting of parameters: the number of
firefly N, the maximum of attractiveness b0, the light
absorption coefficient g, step-size a, Maximum iterations maxiter, iter = 0.
Step 3 Initialize the location of firefly in the solution
space of the neighborhood radius ε of synchronization.
Step 4 Do clustering respectively based on the synchronous in ε that each firefly corresponding.
Step 4.1 Find ε-neighborhood closures of protein
objects of matrix X. Objects that belong to the
same closures are divided into a cluster, and then
mark those objects.
Step 4.2 If all points are marked, return to the
result of clustering, otherwise the unmarked
objects couple with the objects in its ε-neighborhood according to the formula (2), and then go
to step 4.1.
Step 5 The light intensity of fireflies are assigned by
the calculation result of the objective function (9)
according to the clustering result. Compare the
brightness of fireflies, if Ii >Ii, calculate the attractiveness according formula (5), and then update the
location of firefly i according to the formula (6).
Step 6 iter = iter+1;
Step 7 If iter <= maxiter, go to Step 4, otherwise
output the clustering result that the firefly with the
highest light intensity.
The time complexity of algorithm
The time complexity of the SHC algorithm is O(T·n^2) in a
certain neighborhood radius, where n is the number of
nodes, T is the number of synchronization to form ε-neighborhood closures. Assuming that the number of dynamic
interaction of synchronization of data to form ε-neighborhood closures will not change in different neighborhood
radiuses, the time complexity of the SHC algorithm is O
(k·T·n^2), k is the number of iterations of searching for the
optimal ε-neighborhood radius based on the hierarchical
search. Replacing the hierarchical search with FA results in
the decrease of k, it enhances the efficiency of the algorithm.
Results and discussions
Flow chart of the algorithm
Analysis of experiment parameters
Figure 2 is the flow chart of the improved synchronization-based hierarchical clustering algorithm.
The detailed procedures of the improved SHC algorithm are as follows.
Step 1 Construct a similarity matrix A of protein
objects, and then get Laplacian matrix L of the matrix A.
We set the weigh h = 0.5 to balance the aggregation coefficient of the edge and the weighted aggregation coefficient of the edge in Eq. (8) in the preprocessing. The
setting of parameters of the FA: the maximum of attractiveness b0 = 1, the light absorption coefficient g = 1, the
step-size a = 0.9, we set r = 0.8 in the objective function.
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Figure 2 Flow chart of the improved SHC algorithm.
The value of r in the objective function of the FA is
important to evaluate the result of clustering. Aiming at
reflecting the correlation between fval and f-measure in
different r, we calculate the Pearson correlation coefficient of fval and f-measure in 20 values that distribute
evenly in the region of 1 to 6 of the neighborhood
radiuses. The result is shown in Table 1 the fval and fmeasure correlation is extremely linear when r = 0.8.
Thus we set r = 0.8.
The Pearson correlation coefficient is shown as:
n
Xi − X¯
Yi − Y¯
1 r=
n − 1 i=1
SX
SY
(10)
Where X¯ , Y¯ and SX, SY represent the mean value and
the variance of X and Y, respectively.
With practical consideration, we set the searching
number for the optimal threshold of the neighborhood
radius of synchronization small values. As the value of
step-size a in FA is important to the result, we calculate
20 times to get the average value of the maximum
objective function in different a, which is shown in
Table 2. Tests are carried on n = 6, maxiter = 30 cases.
The average of the maximum objective function value is
optimal when a = 0.9. Thus we set step-size a = 0.9.
The performance comparison on different optimization
algorithms
In the experiments, the dataset of PPI networks was
downloaded from MIPS database [40], which consists of
two sets of data: one is the experimental data which
contains 1376 protein nodes and the 6880 interactive
Table 1 Comparisons of the Pearson correlation coefficient between fval and f-measure in different r
r
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
r
50.9059
51.9654
52.0163
52.0588
52.0933
52.1201
52.1396
52.1521
52.1580
52.1574
52.1508
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Table 2 The average of maximum objective function values of different a for 20 times clustering (value)
a
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
value
94.4592
95.7940
95.4177
95.4546
95.7102
96.1161
95.2840
95.7408
95.7408
96.2709
95.7664
Table 3 The experimental parameters of PSO, GA and FA algorithms
PSO
The global acceleration coefficient (c1) = 2
The local acceleration coefficient (c2) = 2
GA
The crossover probability (pcro) = 0.8
The mutation probability (pmut) = 0.085
FA
The maximum of attractiveness b0 = 1
The step-size a = 0.9
The light absorption coefficient g = 1
Table 4 The maximum objective function value of 10 times (value) of the FA, PSO, and GA algorithms
Algorithm
1
2
3
4
5
6
7
8
9
10
PSO
96.2709
96.2709
91.6680
96.2709
96.2709
91.6680
91.6680
96.2709
96.2709
96.2709
GA
96.2709
95.8277
95.9618
96.1602
96.0500
95.9618
95.9655
96.0500
96.2709
96.2709
FA
96.2709
96.2709
96.2709
96.2709
96.2709
96.2709
96.2709
96.2709
96.2709
96.2709
protein-pairs, which is considered the training dataset;
the other describes the result that the proteins belong to
identical functional module, which is regarded as the
standard dataset [41], containing 89 clusters.
Inspired by the swarm optimization algorithms [42-45],
we use them to search for the optimal threshold of the
neighborhood radius of synchronization. The experimental
parameters of PSO, GA and FA algorithms are shown in
Table 3. The parameters of PSO and GA are set empirically based on the references [46,47]. We also calculate the
maximum objective function values for 10 times and the
average of the maximum objective function value over 20
times of the FA, the PSO, and the GA, which is shown in
Table 4 and Table 5. The plots of the optimal objective
function value with the number of iterations are depicted
in Figure 3. The FA algorithm always converges to the
optimal value fast. However, the PSO algorithm falls into a
low value sometimes, the GA algorithm gets a higher
value always. The FA performs best when considering
convergence speed and global optimization ability
comprehensive.
Precision, recall, and f-measure are employed as the
metric for clustering in this study. Precision [48] is the
ratio of the number of maximum matching nodes in training with standard database to the number of training
nodes. Recall [48] is the ratio of the largest number of
nodes in training matched the standard database to the
number of nodes in the standard database. Precision and
recall are defined as Eqs. (11)- (12), respectively while
Table 5 The average of the maximal objective function
value of PSO, GA and FA algorithms on 10 times
Algorithm
PSO
GA
FA
Value
94.8900
96.0790
96.2709
F-measure, the harmonic mean of precision and recall is
defined as Eq. (13).
precison (C—F) =
(11)
MMS (C, F)
|F|
(12)
precision · recall
precision + recall
(13)
recall (C—F) =
f - measure =
MMS (C, F)
|C|
where C is the set of cluster results of training database, F stands for the set of cluster results of MIPS
Figure 3 Plots of the optimal objective function value with the
number of iterations of the FA, PSO and GA. (a) Comparison of
precision value (b) Comparison of recall value (c) Comparison of fmeasure value
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database, |C| represents the number of cluster nodes in
training database, |F| represents the number of cluster
nodes in standard database, MMS represents the number of maximum matching nodes in training database
with standard database.
The running time of SHC algorithm and the proposed
algorithm are proportional to the searching number of
times, when the searching number of times is big, the
algorithms are impractical. So we compare the two algorithms in 20 times searching. The proposed method is
compared with SC algorithm and the SHC algorithm in
terms of precision, recall and f-measure, respectively.
The ISHC algorithm converges to the optimal threshold of the neighborhood radius of synchronization steadily when the searching number is large in Table 4. In
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order to obtain a more intuitive and obvious result, we
reduce the searching number in Figure 4. We can see
from the Figure 4 and Table 6 that the performance of
the ISHC algorithm is better than the SC algorithm and
the SHC algorithm in precision, recall and f-measure.
We compare our proposed algorithm with the SHC algorithm in 20 times searching as in Figure 4, the result of
our proposed algorithm is not stable sometimes. We also
compare our proposed algorithm with some classical algorithms shown in Table 6. In these algorithms we listed,
the result of our proposed algorithm performs best.
Conclusion
The sync algorithm is a novel clustering algorithm based
on the model of synchronous dynamics, which can detect
Figure 4 The improved algorithm compared with spectral clustering algorithm and SHC in precision, recall and f-measure.
Lei et al. BMC Genomics 2015, 16(Suppl 3):S3
http://www.biomedcentral.com/1471-2164/16/S3/S3
Page 9 of 10
Table 6 Comparison of precision, recall and f-measure among ISHC, SHC, SC and other algorithms
Algorithm
precision
recall
f-measure
SHC[31,32]
0.4447
0.3430
0.3873
SC[13]
0.3612
0.3555
0.3584
MCL[12]
0.3569
0.3879
0.3717
Newman[8]
0.4665
0.4186
0.4413
RNSC[9]
0.4067
0.4696
0.4359
ISHC
0.6624
0.3620
0.4673
clusters with arbitrary shape and size and has the antinoise ability. However, the running time of the algorithm
consists of two parts primarily: The dynamic interaction
time of synchronizing data and the process of determining the optimal synchronous neighborhood radius, which
is too long to process large-scale data. The SHC algorithm proposes the concept of neighborhood closures
reducing dynamic interaction time of the sync algorithm.
In our proposed algorithm, the efficiency and accuracy is
further improved by using the FA to determine the optimal thresholds of neighborhood radius of synchronization. The recall, precision and f-measure of our proposed
algorithm are improved compared with SC and SHC
algorithms. In future, we are intending to seek a more
suitable model of synchronous dynamics for PPI data
clustering to improve the effect of the algorithm, also the
time complexity is still need to be decreased.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
Competing interests
The authors declare that they have no competing interests.
15.
Declarations
The funding for the publication charges comes from: the National Natural
Science Foundation of China (61100164, 61173190), Scientific Research Startup Foundation for Returned Scholars, Ministry of Education of China ([2012]
1707) and the Fundamental Research Funds for the Central Universities,
Shaanxi Normal University (GK201402035, GK201302025).
This article has been published as part of BMC Genomics Volume 16
Supplement 3, 2015: Selected articles from the 10th International
Symposium on Bioinformatics Research and Applications (ISBRA-14):
Genomics. The full contents of the supplement are available online at http://
www.biomedcentral.com/bmcgenomics/supplements/16/S3.
Authors’ details
1
School of Computer Science, Shaanxi Normal University, Xi’an, Shaanxi
710062, China. 2School of Electronics Engineering and Computer Science,
Peking University, Beijing,100871, China. 3Division of Biomedical Engineering,
University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada.
16.
17.
18.
19.
20.
21.
22.
Published: 29 January 2015
References
1. Watts DJ, Stroqatz SH: Collective dynamics of ‘small-world’ networks.
Nature 1998, 393(6684):440-442.
2. del Sol A, O’Meara P: Small-world network approach to identify key
residues in protein-protein interaction. Proteins: Structure, Function, and
Bioinformatics 2005, 58(3):672-82.
3. Wang J, Li M, Deng Y, Pan Y: Recent advances in clustering methods for
protein interaction networks. BMC Genomics 2010, 11(Suppl 3):S10.
23.
24.
25.
Li M, Wang J, Chen J, Cai Z: Identifying the Overlapping Complexes in
Protein Interaction Networks. International Journal of Data Ming and
Bioinformatics 2010, 4(1):91-108.
Girvn M, Newman MEJ: Community structure in social and biological
networks. Proceedings of the National Academy of Science 2002,
99(12):7821-6.
Wasserman S, Faust K: Social network analysis: methods and applications.
Cambridge: Cambridge University Press; 1994.
Freeman L: A set of measure of centrality based upon betweeness.
Sociometry 1977, 40(1):35-41.
Newman MEJ: Fast algorithm for dectecting community structure in
networks. Physical Review E 2004, 69(6):066133.
King AD, Pržulj N, et al: Protein complex prediction via cost-based
clustering. Bioinformatics 2004, 20(17):3013-20.
Palla G, Derényi I, et al: Uncovering the overlapping community structure
of complex networks in nature and society. Nature 2005, 435(7043):814-8.
Bader GD, Hogue CW: An automated method for finding molecular
complexes in large protein interaction networks. BMC Bioinformatics 2003,
4:article 2.
van Dongen SM: Graph clustering by flow simulation. PhD thesis, Center
for Math and Computer Science (CWI) 2000.
Ng Andrew Y, Jordan MI, Weiss Y: On spectral clustering: analysis and an
algorithm[C]. Advances in Neural Information Processing Systems Cambridge,
MA: MIT Press; 2001.
Zhao B, Wang J, Li M, Wu F, Pan Y: Detecting Protein Complexes Based
on Uncertain Graph Model. IEEE/ACM Transactions on Computational
Biology and Bioinformatics 2014, 11(3):486-497.
Li M, Wu X, Wang J, Pan Y: Towards the identification of protein
complexes and functional modules by integrating PPI network and gene
expression data. BMC Bioinformatics 2012, 13(1):109.
Ramadan E, Osgood C, Pothen A: Discovering overlapping modules and
bridge proteins in proteomic networks. Proc. ACM Int’l Conf. Bioinformatics
and Computational Biology (BCB ‘10) 2010, 366-369.
Efimov D, Zaki N, Berengueres J: Detecting protein complexes from noisy
protein interaction data. Proc. 11th Int’l Workshop Data Mining in
Bioinformatics 2012, 1-7.
Arnau V, Mars S, Marin I: Iterative cluster analysis of protein interaction
data. Bioinformatics 2005, 21(3):364-378.
Frey BJ, Dueck D: Clustering by passing messages between data points.
Science 2007, 15(5814):972-976.
Feng J, Jiang R, Jiang T: A max-flow based approach to the identification
of protein complexes using protein interaction and microarray data.
Computational Systems Bioinformatics 2008, 7:51-62.
Inoue K, Li W, Kurata H: Diffusion model based spectral clustering for
protein-protein interaction networks. PLoS ONE 2010, 5(9):e12623.
Qi YJ, Balem F, Faloutsos C, Klein-Seetharaman J, Bar-Joseph Z: Protein
complex identification by supervised graph local clustering.
Bioinformatics 2008, 24(13):250-268.
Leung HC, Yiu SM, Xiang Q, Chin FY: Predicting protein complexes from
PPI Data: A core-attachment approach. J Computational Biology 2009,
16(2):133-144.
Li M, Wang J, Chen J: A fast agglomerate algorithm for mining functional
modules in protein interaction networks. International Conference on
BioMedical Engineering and Informatics 2008, 1:3-7.
Wang J, Li M, Chen J, Pan Y: A fast hierarchical clustering algorithm for
functional modules discovery in protein interaction networks. IEEE
Transactions on Computational Biology and Bioinformatics 2011, 8(3):607-620.
Lei et al. BMC Genomics 2015, 16(Suppl 3):S3
http://www.biomedcentral.com/1471-2164/16/S3/S3
26. Li M, Chen J, Wang J, H B, C G: Modifying the DPClus algorithm for
identifying protein complexes based on new topological structures. BMC
Bioinformatics 2008, 9:398.
27. Wang J, L B, L M, Pan Y: Identifying protein complexes from interaction
networks based on clique percolation and distance restriction. BMC
Genomics 2010, 11(Suppl 2):S10.
28. Ren J, Wang J, Li M, Wang L: Identifying protein complexes based on
density and modularity in protein-protein interaction network. BMC
Systems Biology 2013, 7:S12.
29. Wu S, Lei XJ, Tian JF: Clustering PPI network based on functional flow
model through artificial bee colony algorithm. Proc Seventh Int’l Conf,
Natural Computation 2011, 92-96.
30. Böhm C, Plant C, Shao JM, et al: Clustering by synchronization. Proceedings
of ACM SIGKDD’10, Washington 2010, 583-592.
31. George K, Eui-Hong H, Vipin K: CHAMELEON A hierarchical clustering
algorithm using dynamic modeling. IEEE Comput 1999, 32:68-75.
32. Huang JB, Kang JM, Qi JJ, Sun HL: A hierarchical clustering method based
on a dynamic synchronization model. Science China: Information Science
2013, 43(5):599-610.
33. Yang XS: Nature-Inspired metaheuristic algorithms [M]. Luniver Press 2008,
83-96.
34. Yang XS: Firefly algorithm, stochastic test functions and design
optimization. Int J Bio-Inspired Comput 2010, 2(2):78-84.
35. Krishnanand KN, Ghose D: Detection of multiple source locations using a
firefly metaphor with applications to collective robotics[C]. Proceeding of
IEEE Swarm Intelligence Symposium Piscataway: IEEE Press; 2005, 84-91.
36. Acebron JA, Bonilla LL, Vicente CJP, et al: The Kuramoto Model: A simple
paradigm for synchronization phenomena. Rev Mod Phys 2005,
77(1):137-49.
37. Aeyels D, Smet FD: A mathematical model for the dynamics of clustering.
Physica D: Nonlinear Phenomena 2008, 273(19):2517-2530.
38. Radicchi F, Castellano C, Cecconi F, et al: Defining and identifying
communities in networks. Proceeding of the National Academy of Sciences
of the USA 2004, 101(9):2658-63.
39. Karaboga D, Basturk B: A powerful and efficient algorithm for numerical
function optimization: artificial bee colony (ABC) algorithm. Journal of
Global Optimization 2007, 39(3):459-471.
40. Güldener U, Münsterkötter M, Kastenmüller G, et al: CYGD: the
comprehensive yeast genome database. Nucleic Acids Research 2005, 33:
D364-D368.
41. Mewes HW, Frishman D, Mayer KF, et al: MIPS: analysis and annotation of
proteins from whole genomes in 2005. Nucleic Acids Research 2006, 34:
D169-D172.
42. Lei XJ, Tian JF, Ge L, Zhang AD: The clustering model and algorithm of
PPI network based on propagating mechanism of artificial bee colony.
Information Sciences 2013, 247:21-39.
43. Lei XJ, Wu S, Ge L, Zhang AD: Clustering and Overlapping Modules
Detection in PPI Network Based on IBFO. Proteomics 2013, 13(2):278-290,
Jan.
44. Lei XJ, Wu FX, Tian JF, Zhao J: ABC and IFC: Modules Detection Method
for PPI Network. BioMed Research International 2014, 2014, Article ID
968173, 11 pages, doi:10.1155/2014/968173.
45. van der Merwe DW, Engelbrecht AP: Data clustering using particle swarm
optimization[C]. Proc of 2003 Congress on Evolutionary Computation (CEC’03)
2003, 215-220.
46. Maulik U, Bandyopadhyay S: Genetic algorithm-based clustering
technique [J]. Pattern Recognition 2000, 33:1455-1465.
47. Shi YH, Eberhart RC: Parameter Selection in Particle Swarm Optimization.
Lecture Notes in Computer Science 1998, 1447:591-600.
48. Zhang AD: Protein interaction networks. New York, USA: Cambridge
University Press; 2009.
doi:10.1186/1471-2164-16-S3-S3
Cite this article as: Lei et al.: Clustering PPI data by combining FA and
SHC method. BMC Genomics 2015 16(Suppl 3):S3.
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