Network Properties for Ranking Predicted miRNA Targets in Breast Cancer.
ABSTRACT MicroRNAs control the expression of their target genes by translational repression and transcriptional cleavage. They are involved in various biological processes including development and progression of cancer. To uncover the biological role of miRNAs it is important to identify their target genes. The small number of experimentally validated target genes makes computer prediction methods very important. However, state-of-the-art prediction tools result in a great number of putative targets with an unpredictable number of false positives. In this paper, we propose and evaluate two approaches for ranking the biological relevance of putative targets of miRNAs which are associated with breast cancer.
- SourceAvailable from: Frank Slack
Article: The role of microRNAs in cancer.[Show abstract] [Hide abstract]
ABSTRACT: Cancer is a complex and dynamic disease, involving a variety of changes in gene expression and structure. Traditionally, the study of cancer has focused on protein-coding genes, considering these as the principal effectors and regulators of tumorigenesis. Recent advances, however, have brought non-protein-coding RNA into the spotlight. MicroRNAs (miRNAs), one such class of non-coding RNAs, have been implicated in the regulation of cell growth, differentiation, and apoptosis . While their study is still at an early stage, and their mechanism of action along with their importance in cancer is not yet fully understood, they may provide an important layer of genetic regulation in tumorigenesis, and ultimately become valuable therapeutic tools.The Yale journal of biology and medicine 01/2007; 79(3-4):131-40.
Article: MicroRNA: implications for cancer.[Show abstract] [Hide abstract]
ABSTRACT: MicroRNAs (miRNAs) are small RNA molecules that regulate gene expression post-transcriptionally. After the discovery of the first miRNA in the roundworm Caenorhabditis elegans, these short regulatory RNAs have been found to be an abundant class of RNAs in plants, animals, and DNA viruses. About 3% of human genes encode for miRNAs, and up to 30% of human protein coding genes may be regulated by miRNAs. MicroRNAs play a key role in diverse biological processes, including development, cell proliferation, differentiation, and apoptosis. Accordingly, altered miRNA expression is likely to contribute to human disease, including cancer. This review will summarize the emerging knowledge of the connections between human miRNA biology and different aspects of carcinogenesis. Various techniques available to investigate miRNAs will also be discussed.Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin 02/2008; 452(1):1-10. · 2.68 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: MicroRNAs (miRNAs) are approximately 22-nt RNA segments that are involved in the regulation of protein expression primarily by binding to one or more target sites on an mRNA transcript and inhibiting translation. MicroRNAs are likely to factor into multiple developmental pathways, multiple mechanisms of gene regulation, and underlie an array of inherited disease processes and phenotypic determinants. Several computational programs exist to predict miRNA targets in mammals, fruit flies, worms, and plants. However, to date, there is no systematic collection and description of miRNA targets with experimental support. We describe a database, TarBase, which houses a manually curated collection of experimentally tested miRNA targets, in human/mouse, fruit fly, worm, and zebrafish, distinguishing between those that tested positive and those that tested negative. Each positive target site is described by the miRNA that binds it, the gene in which it occurs, the nature of the experiments that were conducted to test it, the sufficiency of the site to induce translational repression and/or cleavage, and the paper from which all these data were extracted. Additionally, the database is functionally linked to several other useful databases such as Gene Ontology (GO) and UCSC Genome Browser. TarBase reveals significantly more experimentally supported targets than even recent reviews claim, thereby providing a comprehensive data set from which to assess features of miRNA targeting that will be useful for the next generation of target prediction programs. TarBase can be accessed at http://www.diana.pcbi.upenn.edu/tarbase.RNA 03/2006; 12(2):192-7. · 5.09 Impact Factor
Hindawi Publishing Corporation
Advances in Bioinformatics
Volume 2009, Article ID 182689, 7 pages
NetworkPropertiesforRankingPredicted miRNATargets in
J¨ org Linde,1Bj¨ ornOlsson,2andZelminaLubovac2
1Leibniz-Institute for Natural Product Research and Infection Biology, Hans-Knoell-Institute, Beutenbergstraße 11A,
07745 Jena, Germany
2School of Life Sciences, Systems Biology Research Centre, University of Sk¨ ovde, P.O. Box 408, 54128 Sk¨ ovde, Sweden
Correspondence should be addressed to Zelmina Lubovac, email@example.com
Received 2 August 2009; Accepted 15 November 2009
Recommended by Tolga Can
MicroRNAs control the expression of their target genes by translational repression and transcriptional cleavage. They are involved
in various biological processes including development and progression of cancer. To uncover the biological role of miRNAs
it is important to identify their target genes. The small number of experimentally validated target genes makes computer
prediction methods very important. However, state-of-the-art prediction tools result in a great number of putative targets with an
of putative targets of miRNAs which are associated with breast cancer.
Copyright © 2009 J¨ org Linde et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
It has been uncovered that microRNAs (miRNAs) play an
important role in several cellular processes, such as develop-
are crucial in a number of diseases, including cancer [1, 2].
The prediction of their target genes is an important step
towards uncovering the role of miRNAs. However, state-of-
the-art prediction tools result in a large number of putative
target genes, many of which may be false positives.
The small number of experimentally validated target
genes  makes it difficult to determine the number of false
hits . In addition, even if a highly specific prediction
algorithm could be envisaged, we cannot be sure which
target gene is relevant in a biological sense. For example, a
predicted target might only be seen as relevant if the putative
target gene and the miRNA gene are temporarily and locally
This paper investigates different properties of putative
suggesttheir biological relevance.Thelist of putative miRNA
targets originates from the study by Iorio et al. , where
29 significantly deregulated miRNAs in human breast cancer
were identified. Of these 29 miRNAs, our study focuses on
the five that are most consistently differentially expressed
between normal and breast cancer tissues.
Most existing prediction tools [6–8] use sequence-
based or three-dimensional complex analysis to find target
genes and evolutionary sequence conservation to reduce
the number of false positives. We attempt to complement
these approaches by using more biological background
these putative target genes. As we focus on the targets of
the miRNAs that are involved in breast cancer, we expect
these targets to play important roles in cancer, and thereby
act as interesting candidates as cancer biomarkers. The list
of target genes generated by our approaches provides a
suggested that miRNAs and their targets might be used as
both biomarkers and drug targets .
In a recent study, Liang and Li  investigated the
role of miRNAs in regulation of protein-protein interaction
networks (PPINs). MiRNAs preferentially regulate proteins
which have a higher than average number of interacting
partners in the network. Node degree in a human PPIN
is positively correlated with the number of miRNA target-
site types. It has also been shown that proteins having
many interacting neighbours normally take part in more
2Advances in Bioinformatics
biological processes . Therefore, it is reasonable to
expect that a protein that has more interacting protein
partners is regulated by more transcription factors and more
miRNAs. The use of network topological properties, such as
node degree, to characterise cancer-related proteins is also
supported by the finding that cancer-related proteins have
larger mean degree value than the mean value of the human
Hence, we characterise these properties of the nodes
in the network corresponding to putative target genes
and compare them to the mean values of the network.
In this way we investigate different ways of ranking the
putative target genes according to their roles within the
PPIN. To evaluate different approaches we use a data set
of experimentally validated miRNA target genes  and
a data set of genes which are known to be involved in
breast cancer . We are able to show that the network
properties used in this study contribute differentially to
the ranking approach, and furthermore that our rank-
ing approach is capable to discriminate between genes
involved in breast cancer and genes which seem not to be
2.1. Putative Target Genes of Deregulated miRNAs in Breast
Cancer. Iorio et al.  identified 29 significantly deregulated
miRNAs in human breast cancer. The five most consistently
differentially expressed miRNAs are miR10b, miR125b,
miR145, miR21, and miR155. To generate a list of putative
targets the authors used three tools, namely, miRanda ,
Pictar , and TargetScan . For more reliable results,
only targets predicted by at least two of the tools were
included in the result. Thus they generated a list of 719
putative target genes for these five miRNAs. (the list is
available as supplementary material in Iorio et al.  ) We
had to exclude some of these genes for either of the following
reasons: (1) they are from the mouse or rat genome and no
human homologue is known, (2) they are not included in
the human PPIN. Out of the 719 putative target genes, 465
passed the exclusion criteria.
2.2. Experimentally Validated miRNA Target Genes. We used
in order to compare them to putative targets. In March
2008 TarBase contained 461 validated human miRNA target
sites from 418 different genes. Out of those 418 genes,
213 passed the exclusion criteria (see above). For the five
most consistently differentially expressed miRNAs identified
by Iorio et al. , the TarBase search resulted in 19
experimentally validated target genes. The list of putative
target genes generated by Iorio et al.  does not include
any of these validated target genes for these five miRNAs.
However, out of the 418 validated human target genes, 22
can be found in the list of Iorio et al. . Note that these
are targets of other miRNAs than those five most consistently
2.3. Protein-Protein Interaction Network. The human PPIN
was downloaded from the Human Protein Reference
Database ( accessed in September 2008)  which contains
manually curated protein-protein interactions between 9162
proteins. As identifiers we used Gene Symbols. Network
visualization and analysis tool Cytoscape  was used to
find the putative target genes for deregulated miRNAs in
breast cancer within PPIN.
2.4. Breast Cancer Genes. We downloaded genes which are
Gene Database ( accessed in April 2008) . The database
is manually curated and the data is extracted from literature
search. We downloaded a list of 72 genes which we use to
evaluate our results. However, we could not use all of them
because they were not included in the human PPIN, nor
annotated by the GO categories. Out of those 72 genes, 27
are located in the human PPIN but only one is among the
465 putative target genes we used for the PPIN analysis.
2.5. Network Measures. The following chapter describes
network properties used in this study. The igraph package
 of the statistical language R  was used to calculate
each network property value for each node within the
PPIN. Mean values were calculated for the following sets of
nodes: the whole PPIN, the putative miRNA targets, and the
validated miRNA targets. The Mann-Whitney-Wilcoxon test
of the different data sets for all four network properties.
2.6. Degree. The degree value of a vertex is the number of
directed neighbours of the vertex.
2.7. Betweenness Centrality. Betweenness centrality has been
applied in the context of social networks, to measure the
centrality and influence of a person or a group . The
betweenness centrality of a node v was originally defined by
Freeman  as the number of shortest paths (also called
geodesics) between other nodes that pass through v and it is
i,j∈V: i/ = j,i/ =v,j / =v
where givj is the number of shortest paths linking i and
j that contain v, and gij is the total number of shortest
paths between i and j. High-betweenness nodes occur on
large number of nonredundant shortest paths between other
it may disconnect different parts of the network completely.
Thus, such nodes may be thought of as potential bridges
between modules in network and have most influence on the
ness centrality which is defined as the mean shortest path
Advances in Bioinformatics3
length between a vertex and all other vertices reachable from
it. It is defined as
n − 1
where d(v,t) is the shortest distance between v and t, and
n is the number of reachable vertices from v. Vertices which
have mean short distances to other ones in the network have
greater closeness centrality values.
2.9. Clustering Coefficient. The clustering coefficient value is
the fraction of connected neighbours of a vertex i divided
by the number of all possible connections of the neighbours
of i . In other words it measures how close a vertex and
its neighbours are to be totally connected and thus form a
“clique.” Many cliques contribute to a small world network
because you can reach every vertex on a short path . The
times the number of edges between the neighbours divided
by the number of all possible edges between the neighbours
n(n − 1),
where N(v) is the number of edges between the neighbours
of v, and n is the number of neighbours of v.
2.10. Ranking Approaches. In this study we applied a simple
ranking approach to access the biological relevance of a
putative miRNA target gene. We compared all four network
property values of a putative target gene to the mean values
within the whole PPIN. For degree, betweenness centrality
and closeness centrality greater mean values were found in
both the validated and putative target genes compared to the
whole PPIN. For this reason we checked for each putative
target if it has a greater value than the mean of the PPIN for
these three properties. For clustering coefficient the putative
targets have a greater mean value than the mean of the PPIN
while the putative targets have a smaller one. For this reason
we test both a “greater than” and a “smaller than” criteria for
this network property. The ranking approach and data sets
used in this study are summarised in Figure 1.
3.1. Ranking according to PPIN Properties. In order to
investigate potential methods to rank putative target genes
target sets (see Section 2). For each data set we calculated
the mean degree k, betweenness centrality (Cb), closeness
centrality (Closeness), and clustering coefficient values (Cc)
(see Table 1).
Both the validated and the putative target data sets are
characterised by greater mean degree values than PPIN,
which suggests that this property is the most consistently
different from the whole human PPIN. A similar result
was obtained for betweenness centrality, where the networks
of validated and putative targets show higher betweenness
Table 1: Mean values of network properties. The table summarises
the values of different network properties for three datasets:
the whole PPIN, the putative targets, and the validated targets.
Abbreviations: k: degree, Cb: betweenness centrality, Closeness:
closeness centrality, CC: clustering coefficient.
centrality than PPIN, and the corresponding value for
validated targets is significantly higher than for the putative
targets. The data set of validated targets is characterised by
the highest mean betweenness centrality which suggests the
presence of hubs among the validated targets to a greater
extent compared to other networks.
The mean closeness centrality values do not differ sig-
nificantly between any of the datasets. The mean clustering
coefficient is slightly smaller for the validated target sets but
greater for the putative ones, compared to the mean value of
the whole PPIN. In contrast, the mean degree is greater for
the validated targets than for the putative targets.
This result is highly interesting and adds additional
evidence to what has been proposed by Liang and Li
. In short, they suggested that different types of hub
proteins have different miRNA targeting propensity, that is,
intramodular hubs (usually termed modules) do not tend
to contain miRNA target sites, whereas intermodular hubs
often contain several miRNA target sites. Intermodular hubs,
which are often involved in a variety of cellular processes,
such as transcription regulation and proliferation, are hereby
suggested by Liang and Li  to play a more important role
for miRNA regulation than their counterparts . In this
study we were able to show that also betweenness centrality
may be useful as a complement to existing properties, as
validated target genes show significantly higher values for
To test the significance of these results the Mann-
Whitney-Wilcoxon (MWW) signed rank test  was used
The result is a P-value which is the probability of observing
these differences between mean values by chance. Pairwise
results are shown in Table 2. For both the mean degree and
betweenness centrality values, all pairwise differences are
significant. The closeness centrality values differ significantly
between the whole PPIN and both the validated and putative
and validated targets is not significant. The differences for
the mean clustering coefficient values are only significant
between the whole PPIN and the data set of putative targets.
In the next step we studied which network properties
or combinations of network properties contribute most to
the ranking. In order to perform that, we calculated how
many vertices have greater/smaller values (depending on
how the mean property of validated and putative targets
differs from the mean property of the whole PPIN, see
4Advances in Bioinformatics
For each node:
465 within PPIN
213 within PPIN
-Average value for
in each dataset
-Best genes ranking 1
-Best genes ranking 2
-Wost genes ranking 1
Ranking 1Ranking 2
-Deg > mean deg ?
-Clos > mean clos ?
-Bet > mean bet ?
-Clust > mean clust ?
-Deg > mean deg ?
-Clos > mean clos ?
-Bet > mean bet ?
-Clust < mean clust ?
Venn diagram 1
Venn diagram 2
Figure 1: Overview of the ranking approach.
Table 2: Significance test for network properties. The P-value
indicates the probability of observing the different mean values by
chance. All tests are two tailed. Bold values indicate non significant
results (P > .05). Abbreviations, k: degree, CB: betweenness
centrality, CC: clustering coefficient, Closeness: closeness centrality.
PPIN versus putative
PPIN versus validated
validated versus putative 0.0470
Materials and methods) than the mean values for all four
network properties and all 12 combinations of them. For
easier interpretation this is visualised with the help of
Venn Diagrams. Such diagrams are shown for both the
“greater than” criterion (Figure 2(a)) and “smaller than”
criterion (Figure 2(b)) regarding the clustering coefficient
(see Section 2).
For further evaluation we located the 22 validated
miRNA targets and the one known breast cancer gene
which are members of the 460 putative targets in the Venn
diagrams. Figure 2 shows in which subsets these proteins are
located. The diagrams show that the putative targets are not
members of a distinct subset of a combination of network
properties. Rather, they can be found in different kinds of
subsets. Generally, however, fewer putative targets can be
found when more properties are combined.
In Figure 2(a) there are 14 validated out of 159 putative
targets within the sets of degree, while in Figure 2(b) there
are 13 out of 158. Thus degree maximizes the ratio of
maximum validated targets and minimum putative targets.
All (Figure 2(a)) or all but five (Figure 2(b)) putative
targets are located in the subset generated based on closeness
centrality. Again, closeness centrality does not seem to
contribute a lot to the ranking, using our approach.
3.2. Literature Analysis. Literature research was done for the
following sets of genes
(1) Ten of the best ranked genes using the “smaller than”
criterion for clustering coefficient values.
(2) Ten of the best ranked genes using the “greater than”
criterion for clustering coefficient values.
(3) Ten of the worst ranked using the “smaller than”
criterion for clustering coefficient values.
Advances in Bioinformatics5
Figure 2: Venn diagram showing overlapping of network properties. (a) A criterion “greater than” was used for clustering coefficient. (b) A
criterion “smaller than” was used for clustering coefficient. For example, the 28 in the grey section of (a) indicates that there are 28 putative
targets which have a greater network property value than the mean value of the whole PPIN for all four properties used and are thus best
We used the GeneCards  Database to gain information
about genes and diseases in which they are involved.
For every gene the database shows a table which sum-
marises results of the literature research. For 92 diseases
it is given a score of the relevance of the disease to this
gene which is based on the analysis of co-occurrences of
the gene and the disease in Medline articles. If there exist
cancer-related diseases among the ten highest scored diseases
a maximum of three of them is shown in Table 3 (column 2).
Furthermore, the number of articles in which the gene and
the words “breast cancer” occur together is shown (Table 3,
column 3). To assess the statistical significance of this finding
the Novoseek score  of the relevance of the disease to
this gene is given. (Table 3, column 4) This score compares
the number of documents in which the gene name and the
words “breast cancer” occur together with the number of
documents where both appear independently based on a
hypergeometric distribution. The more co-occurrences are
found in relation to the number of expected, the more
unlikely it is that the gene and the disease occur together
by chance. The absolute numbers of the score are not
meaningful, but they help to compare the different ranking
Table 3 summarises results of the literature research. The
first and the second parts of the table show results for the
top ranked genes for the “smaller than” and “greater than”
criterion (top ten ranks), respectively. The third part shows
results for the ten lowest ranked genes. The putative targets
in the first part are mostly involved in functions such as
transcription regulation or signal transduction. For almost
all of them there are cancer-related articles among the top
ten articles of diseases in which the genes are mentioned.
Moreover breast cancer is often among the top three of
these types of cancer-related articles. Especially for the genes
SHC1 and IRS1 there were 40 and 96 relevant articles found,
respectively. This might imply that this approach is capable
of finding breast cancer related genes. Only for the protein
NEDD9 there was no cancer related article found. However,
only for five of the ten best ranked putative targets (using
the “smaller than” criterion) were any cancer related article
among the top ten disease related articles.
No breast cancer related article was found for any of the
ten lowest ranked genes, and for only one of them was a
cancer related article among the ten top-ranked articles. This
shows that our approach is able to rank genes which are
involved in breast cancer highly while genes which are not
known to be involved in breast cancer are lowly ranked.
The NovoSeek score seems to be quite conservative in
genes were identified whose occurrence in breast cancer
related articles does not happen by chance. This is not the
case for the second ranking approach and the worst ranked
In this study we used network properties of putative miRNA
target genes in an attempt to rank them according to these
properties and to identify the biologically plausible targets.
We characterised a set of properties within the whole human
PPIN and compared it to the properties of sets of validated
and putative targets. The mean network property values of
both the putative and the validated targets differ significantly
fromthoseof the wholehuman PPIN.This differenceshould
be useful to rank the putative target genes according to
The mean degree values differ most consistently between
the whole human PPIN and all other data sets. Furthermore,
using the “greater than” criterion for degree leads to the
greatest ratio between the number of validated targets in
the data set and the total number of putative targets. This
suggests that using degree for ranking may result in high
6 Advances in Bioinformatics
Table 3: Literature research results for ranking according to PPIN properties. The first part shows information for the ten best ranked
putative targets using the “greater than” criterion for clustering coefficient values and the second part the same for using the “smaller than”
criterion. The last part shows results for the ten worst ranked genes.
Men 2a, breast cancer, men 3
Myeloid leukemia chronic
Choriocarcinoma, tumors, leukemia
Carcinoma, colorectal cancer, tumors
Carcinoma embryonal, breast cancer
Leukemia, tumors, leukemia t-cell
Tumors, cancer, colorectal cancer
Colon cancer, mammary tumor, colon
Carcinoma giant cell, tumors
Colon cancer, pancreatic cancer, tumors
Carcinoma, breast cancer
Carcinoma, cancer, tumor
Another measurement producing data sets that differ
consistently and significantly from the whole human PPIN
is closeness centrality. The mean values suggest that the
putative and validated targets have greater mean closeness
centrality values. However, only a very small number of
vertices have smaller closeness centrality values which makes
it very difficult to rank based on a “greater than” criterion for
the mean value.
The mean clustering coefficient value is greater for the
validated targets but smaller for the putative ones. This is
the largest difference between the putative and the validated
targets and might be used for ranking purposes. However,
the number of putative targets having a smaller clustering
coefficient than the mean one is high, which might lead to
low specificity. Using the “smaller than” criterion for the
clustering coefficient in combination with “greater than” for
degree also leads to a great number of validated targets but
a small number of putative targets. The literature search
suggests that using the proteins that have lower clustering
coefficients than the mean value results in target genes
associated to more cancer related articles than the best
ranked putative targets having larger clustering coefficient
than the mean.
Although we cannot draw a general conclusion that the
validated targets are members of a distinct subset, there is
a tendency that they are found more often in the subsets of
combined properties than in sets where only one criterion
based on one property is satisfied. In both cases there are a
large number of both putative and validated targets within
the subsets generated based on closeness centrality values.
State-of-the-art miRNA target gene prediction tools use
sequence-based or three-dimensional complex analysis to
find target genes and evolutionary sequence conservation to
reduce the number of false positives. However, the number
of false positives is still high and difficult to calculate. This
study attempts to complement these tools by applying a
Advances in Bioinformatics7
ranking approach based on network properties to access the
biological most relevant putative miRNA target genes. For
each putative target we test if a set of network properties is
greater than the mean value of the whole human PPIN (also
smaller than in the case of clustering coefficient).
This methodology is applied to putative targets of
miRNAs in breast cancer. Even though a simple “Boolean”
ranking approach is used, we are able to rank genes involved
in breast cancer highly while genes with a low rank do not
seem to play a role in breast cancer. Future contributions
and thus study if the absolute value of the difference to the
mean value of a network property helps more for ranking.
Furthermore, we are sure that our approach could be more
In a recent work by Yuan et al. , an attempt has been
made to demonstrate the functional connection (coregula-
tion) between clustered miRNAs and the target proteins that
are closely located in PPIN . Hence, the functionality
of miRNAs is analysed according to the topological features
of their target proteins. It would be highly interesting to
apply the similar approach to validated and putative targets
of miRNAs in breast cancer, to investigate if the hypothesis
proposed in  can be validated in cancer-related network,
and also analyse it in relation to other topological aspects,
besides connection and proximity. This would be valuable to
guide the experiments towards the regions of PPINs likely to
modulate cancer-related processes, which may be important
for future work with revealing putative therapeutics targets
in cancer,” Yale Journal of Biology and Medicine, vol. 79, no. 3-
4, pp. 131–140, 2006.
 S. Sassen, E. A. Miska, and C. Caldas, “MicroRNA: implica-
tions for cancer,” Virchows Archiv, vol. 452, no. 1, pp. 1–10,
 P. Sethupathy, B. Corda, and A. G. Hatzigeorgiou, “TarBase: a
comprehensive database of experimentally supported animal
microRNA targets,” RNA, vol. 12, no. 2, pp. 192–197, 2006.
 P. Sethupathy, M. Megraw, and A. G. Hatzigeorgiou, “A guide
through present computational approaches for the identifica-
tion of mammalian microRNA targets,” Nature Methods, vol.
3, no. 11, pp. 881–886, 2006.
 M. V. Iorio, M. Ferracin, C.-G. Liu, et al., “MicroRNA gene
expression deregulation in human breast cancer,” Cancer
Research, vol. 65, no. 16, pp. 7065–7070, 2005.
 J. Kr¨ uger and M. Rehmsmeier, “RNAhybrid: microRNA target
prediction easy, fast and flexible,” Nucleic Acids Research, vol.
34, web server issue, pp. W451–W454, 2006.
 S.-K. Kim, J.-W. Nam, J.-K. Rhee, W.-J. Lee, and B.-T. Zhang,
“miTarget: microRNA target gene prediction using a support
vector machine,” BMC Bioinformatics, vol. 7, article 411, 2006.
 X. Xu, “Same computational analysis, different miRNA target
 H. Liang and W.-H. Li, “MicroRNA regulation of human
protein-protein interaction network,” RNA, vol. 13, no. 9, pp.
 E. Wang, A. Lenferink, and M. O’Connor-McCourt, “Cancer
systems biology: exploring cancer-associated genes on cellular
networks,” Cellular and Molecular Life Sciences, vol. 64, no. 14,
pp. 1752–1762, 2007.
 P. F. Jonsson and P. A. Bates, “Global topological features of
cancer proteins in the human interactome,” Bioinformatics,
vol. 22, no. 18, pp. 2291–2297, 2006.
 R. A. Baasiri, S. R. Glasser, D. L. Steffen, and D. A. Wheeler,
“The breast cancer gene database: a collaborative information
resource,” Oncogene, vol. 18, no. 56, pp. 7958–7965, 1999.
Marks, “Human microRNA targets,” PLoS Biology, vol. 2, no.
11, article e363, 2004.
 A.Krek,D.Gr¨ un,M.N.Poy,etal.,“CombinatorialmicroRNA
target predictions,” Nature Genetics, vol. 37, no. 5, pp. 495–
 B. P. Lewis, I.-H. Shih, M. W. Jones-Rhoades, D. P. Bartel, and
C. B. Burge, “Prediction of mammalian microRNA targets,”
Cell, vol. 115, no. 7, pp. 787–798, 2003.
 G. R. Mishra, M. Suresh, K. Kumaran, et al., “Human protein
reference database—2006 update,” Nucleic Acids Research, vol.
34, database issue, pp. D411–D414, 2006.
 P. Shannon, A. Markiel, O. Ozier, et al., “Cytoscape: a
software environment for integrated models of biomolecular
interaction networks,” Genome Research, vol. 13, no. 11, pp.
 G. Csardi and T. Nepusz, “The igraph software package
for complex network research,” InterJournal, vol. Complex
Systems, p. 1695, 2006.
 R Core Development Team, R: A Language and Environment
for Statistical Computing, R Foundation for Statistical Com-
puting, Vienna, Austria, 2008.
 L. C. Freeman, “Centrality in social networks conceptual
clarification,” Social Networks, vol. 1, no. 3, pp. 215–239, 1979.
 D. J. Watts and S. H. Strogatz, “Collective dynamics of ‘small-
world’ networks,” Nature, vol. 393, no. 6684, pp. 440–442,
of Mathematical Statistics, vol. 18, pp. 50–60, 1947.
 M. Rebhan, V. Chalifa-Caspi, J. Prilusky, and D. Lancet,
“GeneCards: integrating information about genes, proteins
and diseases,” Trends in Genetics, vol. 13, no. 4, p. 163, 1997.
 X. Yuan, C. Liu, P. Yang, et al., “Clustered microRNAs’ coor-
dination in regulating protein-protein interaction network,”
BMC Systems Biology, vol. 3, article 65, 2009.