BioMed Research International

Volume 2013 (2013), Article ID 676328, 9 pages

http://dx.doi.org/10.1155/2013/676328

Research Article

Reducing the Complexity of Complex Gene Coexpression Networks by Coupling Multiweighted Labeling with Topological Analysis

Alfredo Benso,1,2 Paolo Cornale,³ Stefano Di Carlo,¹ Gianfranco Politano,¹ and Alessandro Savino1,2

¹Department of Controls and Computer Engineering, Politecnico di Torino, 10129 Torino, Italy

²Consorzio Interuniversitario Nazionale per l’Informatica, 11029 Verres, Italy

³Department of Agriculture, Forest and Food Sciences, Università degli Studi di Torino, 10124 Torino, Italy

Received 30 April 2013; Accepted 25 July 2013

Academic Editor: Sarah H. Elsea

Copyright © 2013 Alfredo Benso 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.

Abstract.

Undirected gene coexpression networks obtained from experimental expression data coupled with efficient computational procedures are increasingly used to identify potentially relevant biological information (e.g., biomarkers) for a particular disease. However, coexpression networks built from experimental expression data are in general large highly connected networks with an elevated number of false-positive interactions (nodes and edges). In order to infer relevant information, the network must be properly filtered and its complexity reduced. Given the complexity and the multivariate nature of the information contained in the network, this requires the development and application of efficient feature selection algorithms to be able to exploit the topological characteristics of the network to identify relevant nodes and edges. This paper proposes an efficient multivariate filtering designed to analyze the topological properties of a coexpression network in order to identify potential relevant genes for a given disease. The algorithm has been tested on three datasets for three well known and studied diseases: acute myeloid leukemia, breast cancer, and diffuse large B-cell lymphoma. Results have been validated resorting to bibliographic data automatically mined using the ProteinQuest literature mining tool.

1. Introduction

Systems biology typically uses networks to model and discover emergent properties among genes, proteins, and other relevant biomolecules referred to specific phenotypes or diseases.

Theoretical studies have revealed that biological networks share many features with other types of networks such as computer or social networks. They enable the application of several mathematical and computational methods of the graph theory to biological studies [1, 2]. The computational analysis of biological networks has therefore become increasingly used to mine the complexity of cellular processes and signaling pathways.

Many types of biological networks do exist, depending on the information associated to their nodes and edges. In general, they can be classified as directed or undirected networks [3]. In directed networks, nodes are molecules, while edges indicate causal biological interactions among nodes (e.g., transcription and translation regulations [4]). Instead, in undirected networks, an edge indicates a shared property, such as sequence similarity [5], gene coexpression [6–9], protein-protein interaction [10], or term cooccurrence in the scientific literature [11–13].

Undirected gene coexpression networks, coupled with efficient computational algorithms and complemented by the literature mining, may represent a valuable instrument to identify relevant information (e.g., biomarkers) for a particular disease. However, coexpression networks built from experimental expression data are in general large highly connected networks with an elevated number of false-positive interactions (nodes and edges). In order to infer relevant information, the network must be properly filtered and its complexity reduced. Given the complexity and the multivariate nature of the information contained in the network, this requires the development and application of efficient feature selection algorithms to be able to exploit the network topological characteristics.

Several feature selection algorithms have been proposed in the literature: some in the context of generic machine learning techniques [14] and others more specifically designed to work with transcriptome data [15–21]. Following Saeys et al. [21], feature selection methods fall in three main categories, namely, filters, wrappers, and embedded methods.

Filters assess the relevance of features by looking at the intrinsic properties of the data. They do not consider any learning or classification model [19]. Filtering techniques easily scale to very high-dimensional datasets. They are computationally simple and fast, and they are independent of any machine learning model to be applied after filtering. However, a common disadvantage of filtering methods is that most of the proposed techniques are univariate [19], which is a major limitation for transcriptome data analysis. In fact, genes tend to work according to complex gene regulatory networks, and their expression profiles are therefore highly correlated. To overcome this limitation, multivariate filtering techniques to some extent incorporating feature dependencies have been introduced [22, 23]. Unfortunately, they are in general slower and less scalable than univariate techniques, thus preventing their application on genome-wide transcriptome data.

Wrappers [24] and embedded approaches [17] differ from filters since they are designed to work with specific machine learning and classification models. The main difference between the two groups of approaches is that in embedded approaches feature selection is built into the classifier, while wrappers work together with the classifier but are not part of it. In general, these approaches are able to improve the classification accuracy. However, different classification models may highlight different sets of relevant genes, and sometimes genes that might be biologically representative are discarded by the classification model.

In this paper, we exploit gene coexpression networks and network topological analysis to implement an efficient multivariate filtering algorithm attempting to reduce the size of the network under analysis and to identify sets of genes, which have a biological relevance for a given disease. In particular, a multiweighted coexpression network, is built on top of collected expression data in order to efficiently represent correlations among genes in high-dimensional expression datasets. A topological analysis algorithm is then applied to the coexpression network to identify regions of the network with interesting topological properties that may highlight relevant genes for the modeled phenomenon.

We tested the proposed approach on a set of microarray experiments for three well-studied diseases and compared the obtained list of relevant genes with a bibliometric correlation list of genes retrieved resorting to the ProteinQuest [25] tool. Statistical analysis on the obtained results highlighted that the proposed approach is able to strongly reduce the size of the analyzed coexpression networks, while keeping genes that are highly correlated with the target diseases in the scientific literature.

2. Methods and Materials

The network filtering approach proposed in this paper includes two computational steps designed to

(1)organize the expression data into a multiweighted coexpression network that is able to better highlight relationships among nodes compared to single-weighted networks, and (2)analyze the multi-weighted network in order to identify regions of the network with interesting topological properties that may highlight relevant genes for the modeled phenomenon.

2.1. Multiweighted Coexpression Networks

Let us consider a dataset of expression data (e.g., microarray data) for a large number of DNA sequences tested under two different conditions (e.g., healthy tissue versus diseased tissue).

We organize the dataset in the form of a gene expression matrix with rows representing samples and columns representing genes. Each element of the matrix provides the differential expression level of gene (column ) in sample (row ) under the two tested conditions. Among the different ways to compute differential expression, in this paper, we exploit the (binary) logarithm of the ratio between the absolute expression of the gene in the two tested conditions (log ratio). Log ratios tend to be normally distributed [26] and enable to easily normalize expression levels from different samples using standard score ( score) normalization [27]:

where and represent the absolute expression of gene in sample under the conditions and , and and denote the mean and the standard deviation of log ratios of all genes within sample .

Normalized expression data can be used to build a single coexpression network enabling to easily identify genes that are coexpressed within an experiment. For our analysis we exploit a multi-weighted coexpression network () that assigns multiple weights to the network edges to identify different forms of coexpression among genes.

An is an undirected weighted graph , where

(i)vertexes represent genes that are differentially expressed in at least one of the available samples; (ii)edges connect pairs of vertexes () and represent genes that are coexpressed in at least a sample; (iii)a weight function assigns to each edge a vector of weights:

A gene is considered differentially expressed in sample if ; that is, its differential expression is greater than a given threshold required to filter residual noise in the data. If the differential expression of a gene is positive, it means the gene is overexpressed in condition compared to condition . We denote the gene as overexpressed. In the opposite condition, that is, negative differential expression, the gene is instead denoted as silenced.

Exploiting the concept of overexpressed and silenced genes, each edge of the network is labeled with four weights associated to the four combinations of expression conditions; the pair of genes connected by the edge may assume the following:    both genes over-expressed,    gene overexpressed and silenced,    gene silenced and over-expressed, and    both genes silenced. Each weight counts how many times the pair of genes assumes the selected state in the set of samples composing the dataset.

Figure 1 shortly summarizes the steps required to construct an starting from raw preprocessed expression data.

(a) Raw preprocessed microarray expression levels for 4 genes , 6 samples , and 2 conditions

(b) Gene expression matrix for the considered dataset reporting the normalized differential expression level of each gene in each sample. Red cells indicate overexpressed genes; green cells indicate silenced genes. When constructing the GEM, the expression threshold has been set to . is the mean and is the standard deviation of all the genes of the sample

(c) MWNET representing the dataset

Figure 1: Example of construction of a MWNET. Expression data in this example are not from real experiments. They are simply used to show the process required to construct a MWNET starting from raw expression data.

Multi-weighted coexpression networks are the first original contribution of this paper and are able to provide interesting insights about gene relations. Network edges highlight relationships among genes with the weights providing a measure of the strength and the type of the relation. Moreover, by looking at the different edges, additional information can be inferred. Genes connected with edges with high / score (i.e., both genes over-expressed or silenced) may underly a biological behavior in which the two genes enhance/silence each other, which is a common motif in biological networks [28, 29]. Similarly genes connected with edges with high / may identify genes connected with negative loops, which again is a common motif observed in several biological networks [29–31].

2.2. Network Filtering

Multi-weighed coexpression networks built from experimental expression data are in general complex highly connected networks that contain an elevated number of false-positive interactions (edges).

The weights assigned to each edge represent a valuable information to remove interaction with low informative content.

Let us consider two candidate genes A and B connected by an edge in a network built from a set of samples. A uniform weight distribution on the edge (e.g., ) identifies a low informative interaction since genes show differentiated behaviors among the different samples. Differently, if the weights are polarized toward one of the four behaviors (e.g., ), the informative content of the edge increases.

This consideration is exploited to build a filtering mechanism for the selected network. A relevance score () defined according to the following equation is assigned to each gene of the network according to the following equation:

Equation (3) tries to assign high scores to genes that are connected to their neighbors with strong polarization. If two genes are connected with almost uniform weight distribution, the minimum and the maximum weights are similar and the first term of the equation tends to zero thus lowering the score. In all other cases, the more the edges are weighted with a not uniform distribution, the more their score increases.

The relevance score introduced by (3) is used to filter the list of nodes and edges of the network thus reducing its complexity. Setting a threshold on the acceptable relevant scores allows us to remove low relevant genes and their related edges, thus obtaining a filtered list of genes and a reduced network ready for further analyses aiming at identifying interesting network motifs [4, 31–34].

The proposed relevance score is defined in order to highlight those genes that manifest expression changes between the two considered conditions and in a significant number of samples of the considered data set. The quality and the numerosity of the available data are therefore the key issues to properly compute this score. This is even more critical when samples collected for a given disease include different phenotypes. If a given phenotype is not properly represented in the dataset, the risk of ranking its specific markers with low relevant scores becomes high, with the risk of losing important biological information during the filtering process that could provide unexpected leads for biologic or therapeutic insights. Nevertheless, this is a common drawback of all machine learning and statistical methods that can only be mitigated by increasing the number of collected samples and carefully selecting them in order to be representative for the considered phenomenon.

Moreover, the threshold used to filter the network is a good instrument to deal with the risk of loosing significant genes. A tight threshold will in general filter the presence of different phenotypes. It enables to obtain a smaller network representative of the common properties of the considered disease, regardless of the specific phenotypes. Instead, by relaxing the threshold, also genes that are informative in a reduced set of samples will be included in the filtered network. Researchers may look at this low ranked genes as candidates nodes that are able to highlight specific phenotypes, and therefore conduct further experimental investigations.

3. Results and Discussion

In our experimental design, we tested the proposed network filtering algorithm on three coexpression networks obtained from expression data for three well studied and documented diseases with available on-line datasets.

(1)Acute Myeloid Leukemia Dataset (AML): peripheral-blood samples or bone marrow samples of intermediate-risk AML with a normal karyotype [35]. This dataset includes 14 samples with 43,196 spots (45 K technology) obtained from microarray data available at the Gene Expression Omnibus (GEO), accession number GSE426. The complete list of selected samples is available in Table 1. (2)Breast Cancer (BC): samples of predominantly advanced primary breast tumor [36]. This dataset includes 20 samples with 9,216 spots (9 K technology) obtained from microarray data available at the Gene Expression Omnibus (GEO), accession number GSE3281. The complete list of selected samples is available in Table 2. (3)Diffuse Large B-Cell Lymphoma Dataset (DLCL): a set of samples from patients with diffuse large B-cell lymphoma, the most common subtype of non-Hodgkin’s lymphoma downloaded from a larger dataset of experiments aiming at performing Lymphoma classification [37, 38]. This dataset includes 51 samples with 9,216 spots (9 K technology) obtained from microarray data available at the Gene Expression Omnibus (GEO), accession number GSE60. The complete list of selected samples is available in Table 3.

Table 1: List of samples for the AML dataset. Samples are cDNA 45 K array technology.

Sample number
GEO accession number
Experiment name

1
GSM6259
AML 13

2
GSM6266
AML 28

3
GSM6281
AML 21

4
GSM6284
AML 112

5
GSM6309
AML 32

6
GSM6317
AML 20

7
GSM6318
AML 111

8
GSM6319
AML 18

9
GSM6275
AML 1

10
GSM6285
AML 25

11
GSM6292
AML 105

12
GSM6311
AML 24

13
GSM6335
AML 16

14
GSM6337
AML 114

Table 2: List of samples for the BC data-set. Samples are cDNA 9 K array technology.

Sample number
GEO accession number
Experiment name

1
GSM73756
BC-16 versus NF (svi114)

2
GSM73784
808A versus NF (svi060)

3
GSM73706
107B versus NF (svi032)

4
GSM73726
110B versus NF (svi033)

5
GSM73727
111A versus NF (svi034)

6
GSM73732
111B versus NF (svi035)

7
GSM73734
114A versus NF (svi037)

8
GSM73736
115B versus NF (svi038)

9
GSM73783
710A versus NF (svi056)

10
GSM73764
118B versus NF (svi041)

11
GSM73786
123B versus NF (svi043)

12
GSM73704
206A versus NF (svi045)

13
GSM73708
214B versus NF (svi048)

14
GSM73709
305A versus NF (svi049)

15
GSM73738
308B versus NF (svi050)

16
GSM73776
402B versus NF (svi052)

17
GSM73777
406A versus NF (svi053)

18
GSM73779
708B versus NF (svi054)

19
GSM73697
805A versus NF (svi058)

20
GSM73699
807A versus NF (svi059)

Table 3: List of samples for the DLCL data-set. Samples are cDNA 9 K array technology.

Sample number
GEO accession number
Experiment name

1
GSM2035
DLCL-0047

2
GSM2036
DLCL-0042

3
GSM1958
DLCL-0040

4
GSM1959
DLCL-0036; OCT

5
GSM2037
DLCL-0035

6
GSM1994
DLCL-0034

7
GSM2038
DLCL-0033

8
GSM1995
DLCL-0032

9
GSM1996
DLCL-0031

10
GSM1997
DLCL-0030

11
GSM1998
DLCL-0029

12
GSM1960
DLCL-0028

13
GSM1999
DLCL-0027

14
GSM2039
DLCL-0026

15
GSM2040
DLCL-0025

16
GSM2000
DLCL-0024

17
GSM2001
DLCL-0023

18
GSM2041
DLCL-0021

19
GSM2043
DLCL-0019

20
GSM2044
DLCL-0018

21
GSM2045
DLCL-0016

22
GSM2047
DLCL-0014

23
GSM2048
DLCL-0013

24
GSM2049
DLCL-0012

25
GSM2050
DLCL-0011

26
GSM2051
DLCL-0010

27
GSM2052
DLCL-0009

28
GSM2053
DLCL-0008

29
GSM2055
DLCL-0006

30
GSM2056
DLCL-0005

31
GSM2058
DLCL-0003

32
GSM2059
DLCL-0002

33
GSM2060
DLCL-0001

34
GSM1965
DLCL-0052 ∥lc4b060

35
GSM1967
DLCL-0041 ∥lc4b061

36
GSM1968
DLCL-0039 ∥lc4b039

37
GSM1969
DLCL-0037 ∥lc4b036

38
GSM2072
DLCL-0034 ∥lc8n109

39
GSM1972
DLCL-0033 ∥lc4b034

40
GSM2073
DLCL-0032 ∥lc8n110

41
GSM2074
DLCL-0031 ∥lc8n108

42
GSM2016
DLCL-0028 ∥lc7b025

43
GSM2077
DLCL-0027 ∥lc8n095

44
GSM1974
DLCL-0025 ∥lc4b059

45
GSM2078
DLCL-0024 ∥lc8n096

46
GSM2079
DLCL-0023 ∥lc8n098

47
GSM1976
DLCL-0015 ∥lc4b063

48
GSM1977
DLCL-0011 ∥lc4b030

49
GSM1978
DLCL-0010 ∥lc4b053

50
GSM1979
DLCL-0009 ∥lc4b027

51
GSM1982
DLCL-0002 ∥lc4b033

Samples have been downloaded from the cDNA Stanford Microarray database [39]. All genes without a valid Unigene ID have been discarded. The normalized differential expression for each gene has been computed according to (1) considering the CH1I_MEAN and the CH2I_MEAN mean intensity channels available for each microarray as absolute expression level of each gene, and (1-folding). Since old microarray technologies often used spots duplication, during the network generation we considered as expressed those genes differentially expressed in at least one of their replica on the microarray.

The filtering process has been executed on the three considered datasets applying a quite relaxed threshold of 0.5 on the relevance score of (3).

Table 4 shows the aggregated results in terms of number of genes before and after filtering, highlighting the relevant reduction ratio. The full list of identified genes is instead provided as Supplementary Material to this paper available online at http://dx.doi.org/10.1155/2013/676328 (global_citation_summary.xlsx file—BC Filtered Genes, AML Filtered Genes and DLCL Filtered Genes sheets).

Table 4: Aggregated filtering results for the three considered data-sets.

Original number of genes
Filtered number of genes
Reduction ratio

AML
39,028
505
98.70%

BC
7,531
662
91.20%

DLCL
6,826
115
98.31%

Validation of the proposed filtering algorithm has been performed by comparing the list of filtered genes with data mined from the available scientific literature. It is worth to mention here that this validation phase does not aim at identifying new markers for the considered diseases. By selecting three diseases that have been intensively studied and documented, we aim at confirming how the proposed method is able to identify relevant genes from raw expression profiles that are widely confirmed in the available literature. In order to rely on a large literature dataset, rather then performing manual searches, the literature has been mined resorting to the ProteinQuest bibliography data mining tool [25].

ProteinQuest is an advanced text-mining tool that exploits the web services offered by PubMed to perform advanced semantic searches of scientific papers. It searches for biological terms (e.g., diseases, proteins, genes, miRNAs, etc.) in titles and abstracts as well as in all image captions of all papers stored in Medline. Image captions are extracted, from free full-text articles, using the BFO Java library (http://bfo.com/) on the PDF version of the scientific papers [40]. ProteinQuest is capable of inheriting the PubMed MeSH terms indexing. ProteinQuest text-mining tool searches, in the abstracts and figure captions of all identified publications, for terms belonging to a manually curated protein dictionary based on Entrez MeSH terms. Common ambiguities in the terminology are resolved using a multiple search for more than one alias, as well as the cooccurrence of specific words, which can deny or force the tagging process.

ProteinQuest has been first used to manually refine the obtained lists of filtered genes in order to remove generic oncogenes that cannot be specifically ascribed to a selected disease. For instance, the AML’s filtered genes contain TP53, a tumor suppressor protein crucial in multicellular organisms for cell cycle regulation [41]. TP53 is a clear example of a generic oncogene not specifically marking a particular cancer disease. To enhance the specificity of the network to the selected disease, it has been manually removed after filtering.

As a preliminary validation, we searched using ProteinQuest for all genes that have been cocited with one of the three available diseases submitting the query: “Leukemia, Myeloid, Acute” or “Breast Neoplasm” or “Lymphoma, Large B-Cell, Diffuse.” The query is designed not to lose genes included in subsets such as phenotypes, and subtypes, by resorting to the most general keywords available for each disease. The query selected 10,488 genes extracted from 269,641 analyzed publications, and returned for each gene and for each disease the number of detected co-citations (global_citation_summary.xlsx file—Citation Data sheet—Columns A–D). For each disease, we sorted the list of 10,488 selected genes by their decreasing citation count, assigning to each gene a disease gene citation ranking (global_citation_summary.xlsx file—Citation Data sheet—Columns H–J). Top ranked genes are highly cocited with the disease. Finally, for each disease, we marked those genes present in the list of filtered genes obtained by our filtering algorithm. (global_citation_summary.xlsx file—Citation Data sheet—Columns E–G).

Starting from these citation data, Figure 2 summarizes the preliminary validation performed for the AML dataset. Citation data have been filtered to select the AML filtered genes, only. Resulting data have been sorted by the AML citation rank and the citation rank of each selected gene for each of the three diseases has been plotted. The three citation ranks for each gene are always vertically aligned.

Figure 2: AML preliminary bibliometric validation.

Looking at the left side of  Figure 2, one can notice that top ranked genes for the AML datasets (i.e., high number of co-citations between the gene and the disease) have, in general, a lower rank for the other two disease (i.e., lower number of co-citations), thus giving an indication that the genes selected by our algorithm have a higher bibliometric correlation with the selected disease. This selectivity decreases moving to lower ranked genes. It is worth to remember here that filtering was performed with a quite relaxed threshold that on the one hand limits the selectivity of the filtering process but, on the other hand, preserves low ranked genes that might be important to characterize specific phenotypes of the considered disease. This is, for example, the case for the diffuse large B-cell lymphoma. The considered dataset includes samples for two well-known phenotypes of this disease: GC and activated [37]. Thanks to the relaxed threshold, genes CD38, BCL7A, BCL6, MYB, PI3, CD2, CASP10 that are well known to be differentially expressed across the two phenotypes, even if not at the top of the ranked list, have been preserved in the filtered network, thus enabling to preserve this relevant information across the filtering process.

A similar trend is also confirmed looking at the citation data for the remaining two diseases reported in Figures 3 and 4.

Figure 3: BC preliminary bibliometric validation.

Figure 4: DLCL preliminary bibliometric validation.

In order to perform a more solid statistical validation through the use of bibliometric data, we executed a set of queries on ProteinQuest to understand if, given a disease, the set of genes selected by our algorithm is highly cocited with the disease while showing low citation count with the other diseases. As an example, Algorithm 1 shows the query executed to search for citation relevance of AML genes with AML related publications. The query searches for papers in which at least one of the selected genes is cocited with the AML disease and not cocited either with BC or DLCL diseases. The query produces, for each gene, the number of papers in which the selected condition is respected.

                                                                                                              SELECT

                             (G1 OR G2 OR … OR Gn) ∀ Gene ∈ {AML filtered genes}

                                                                    AND “Leukemia, Myeloid, Acute”

     AND  NOT “Breast Neoplasm” OR … “Lymphoma, Large B-Cell, Diffuse”

Algorithm 1: ProteinQuest query example to obtain citation data for the AML dataset.

Data obtained from the execution of these ProteinQuest queries have been aggregated in Table 5 that reports, for each group of filtered genes, the cumulative citation count for each disease. The full set of data returned by the execution of each query and used to construct Table 5 is available in the disease_citation_heatmaps.xlsx file provided as additional material of this paper. By construction, each query guarantees that citations obtained on each column of the table are disjoint.

Table 5: Citations of groups of filtered genes according to the disease.

Group of genes
Disease
Total

AML
DLCL
BC

Number 1 AML filtered genes
23,248
5,741
17,769
46,758

(49.72), [63.68]
(12.28), [28.56]
(38.00), [17.56]

Number 2 DLCL filtered genes
2,470
10,347
6,180
18,997

(13.00), [6.77]
(54.47), [51.47]
(32.53), [6.11]

Number 3 BC filtered genes
10,787
4,015
77,223
92,025

(11.72), [29.55]
(4.36), [19.97]
(83.92), [76.33]

Total
36,505
20,103
101,172

( ): percentage in rows; [ ]: percentage in columns.

We analyzed data reported in Table 5 using SAS software (version 9.1.3; SAS Institute, Cary, NC, USA). In order to find relationship between genes groups and diseases, frequencies of citations have been analyzed. For each group of genes, pairwise differences among diseases have been performed using the FREQ procedure. Furthermore, Bonferroni adjustment of the obtained values has been carried out with the MULTTEST procedure.

The chi-square test reveals that citations frequencies among diseases (AML, DLCL, and BC) significantly differ by groups of selected genes (; ). Considering the first group of selected genes, the frequency of citations referred to AML (49.79%) significantly differs from both DLCL (; ) and BC (; ) citations (12.28% and 38%, resp.). Similarly, the proportion among citations in the second group of selected genes highlights significant differences between DLCL citations frequency (54.47%) and AML citations (13%; ; ) and between DLCL and BC citations (32.53%; ; ). Finally, greater frequency of citations has been observed in the third group of genes when comparing BC (83.92%) over AML (11.72%; ; ) and over DLCL (4.36%; ; ). The obtained results on frequencies of citations support the ability of the algorithm in selecting appropriate genes group according to the selected disease.

4. Conclusions

In this paper we proposed a multi-weighted network topology, and a related algorithm for its analysis, that is able to filter complex coexpression networks obtained from gene expression data in order to reduce the network size and to retain only those genes and those interactions that are potentially related to a selected disease.

The algorithm has been tested on three public datasets for well-known studied diseases proving its high efficiency in reducing the complexity of the network. Moreover, to show that the filtering process is able to keep nodes, which are relevant for a particular disease, a validation campaign that resorts to bibliometric data mined through the ProteinQuest tool is presented.

The proposed approach represents a valuable starting point to reduce the complexity of complex biological networks in order to perform further analyses aiming at identifying interesting network motifs.

Acknowledgments

This work has been partially supported by (i) Grant no. CUP B15G13000010006 awarded by the Regione Valle d’Aosta for the project Open Health Care Network Analysis and (ii) Grant no. 599.972 awarded by Ministero dell’Istruzione, dell’Università e della Ricerca scientifica, programmi di ricerca 2010-2011 for the project (MIND) Mechanical Characterization of Bone Glass-Ceramic Scaffolds Using Nanoindentation and Numerical Modeling.

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