Generic GA-PPI-Net: Generic Evolutionary Algorithm to Detect

Semantic and Topological Biological Communities

Marwa Ben M’Barek

1,2 a

, Amel Borgi

1,3

, Sana Ben Hmida

2 b

and Marta Rukoz

LIPAH, Facult

e des Sciences de Tunis, Universit

e de Tunis El Manar 2092, Tunis, Tunisia

LAMSADE CNRS UMR 7243, Paris Dauphine University, PSL Research University,

Place du Mar

echal de Lattre deTassigny, Paris, France

Institut Sup

erieur d’Informatique, Universit

e de Tunis El Manar, 1002, Tunis, Tunisia

marta.rukoz@lamsade.dauphine.fr

Keywords:

Community Detection, Biological Networks, PPI Networks, Genetic Algorithm, Heuristic Crossover.

Abstract:

Community detection aims to identify topological structures and discover patterns in complex networks. It

presents an important problem of great signiﬁcance in many ﬁelds. In this paper, we are interested in the

detection of communities in biological networks. These networks represent protein-protein or gene-gene

interactions which corresponds to a set of proteins or genes that collaborate at the same cellular function.

The goal is to identify such semantic and/or topological communities from gene annotation sources such as

Gene Ontology. We propose a Genetic Algorithm (GA) based technique as a clustering approach to detect

communities from biological networks. For this purpose, we introduce four speciﬁc components to the GA: a

ﬁtness function based on a similarity measure and the interaction value between proteins or genes, a solution

for representing a community with dynamic size, an heuristic crossover to strengthen links in the communities

and a speciﬁc mutation operator. Experimental results show the ability of our Genetic Algorithm to detect

communities of genes that are semantically similar or/and interacting.

1 INTRODUCTION

Community detection in networks is one of the most

popular topics of modern network science (Fortunato

and Hric, 2016). It deals with an interesting compu-

tational technique for the analysis of networks. It can

yield useful insights into the structural organization

of a network and can serve as a basis for understand-

ing the correspondence between structure and func-

tion (speciﬁc to the domain of the network).

In this paper, we are interested in detecting com-

munities in biological networks. These networks have

received much attention in the last few years since

they model the complex interactions occurring among

different components in the cell (Pizzuti and Rombo,

2014). We mainly focus on Protein-protein or Gene-

gene interaction networks

known as PPI networks.

Their nodes correspond to proteins or genes and the

https://orcid.org/0000-0002-8307-3533

https://orcid.org/0000-0003-4202-613X

Protein-protein or Gene-gene interaction networks are

mathematical representations of the physical contacts be-

tween proteins or genes in the cell.

edges correspond to pairwise interactions between

genes or proteins. These communities give us an idea

about the perception of the network’s structure. The

ultimate goal in biology is to determine how genes

or proteins encode function in the cell. This work is

multidisciplinary as it brings the ﬁeld of biology and

computer science in the broad sense.

Thus, the goal is to ﬁnd communities of genes

having a biological sense (that participate in the same

biological processes or that perform together speciﬁc

biological functions) from gene annotation sources.

To make this task, we have combined three levels of

information:

1. Semantic level: information contained in biologi-

cal ontologies such as Gene Ontology (GO) (Ash-

burner et al., 2000) and information obtained by

the use of a similarity measure such as GO-based

similarity of gene sets (GS2) (Ruths et al., 2009).

It assesses the semantic similarity between pro-

teins or genes.

2. Functional level: information contained in pub-

lic databases describing the interactions of pro-

teins or genes such as Search Tool for Recur-

Ben M’Barek, M., Borgi, A., Ben Hmida, S. and Rukoz, M.

Generic GA-PPI-Net: Generic Evolutionary Algorithm to Detect Semantic and Topological Biological Communities.

DOI: 10.5220/0009779902950306

In Proceedings of the 15th International Conference on Software Technologies (ICSOFT 2020), pages 295-306

ISBN: 978-989-758-443-5

295

ring Instances of Neighbouring Gene (STRING)

database (Mering et al., 2003).

3. Networks level: information contained in path-

way databases that present community of proteins

or genes such as Kyoto Encyclopedia of Genes

and Genomes (KEGG) database (Kanehisa and

Goto, 2000).

A lot of research effort has been put into community

detection in different academic ﬁelds such as physics,

mathematics and computer science. Meanwhile, var-

ious algorithms based on Genetic Algorithms (GA)

have been proposed. These algorithms are used to

overcome some drawbacks such as scaling up of net-

work size. Indeed, some of the community detec-

tion algorithms are unsuitable for very large networks

and require a priori knowledge about the community

structure, as the number and the size of communi-

ties which is not easy or impossible to obtain in real-

world networks (Tasgin et al., 2007). The algorithms

based on GA are very effective for community detec-

tion especially in very large complex networks (Jiao

et al., 2012). However, the vast majority of optimiza-

tion methods proposed to detect community in PPI

networks use graph topology and do not use similar-

ity measures between proteins or genes (Pizzuti and

Rombo, 2014).

This paper presents a new generic community de-

tection algorithm in PPI networks based on GA. The

proposed GA is parameterized according to the im-

portance affected to each measure criterion (semantic

measure and interaction measure). The aim is to de-

tect communities according to either both criteria, or

only one criterion. The obtained communities could

then be analyzed and compared for a better compre-

hension of the topological and similarity measures

and the relation between them. This work is a general-

ization of a previous method (named as GA-PPI-Net)

(Ben M’barek et al., 2019). Thus, we propose a GA

based approach that allows to ﬁnd communities hav-

ing different sizes using the interaction and/or similar-

ity criterion. Alike the previous proposed algorithms,

the new proposed method uses the similarity mea-

sures as well as the interaction measure between pro-

teins or genes and tries to ﬁnd the best proteins/genes’

community by maximizing the concept of community

measure. The main novelties of the approach can be

summarized as follows. We adopt a lighter represen-

tation of a community with dynamic size than the one

adopted in GA-PPI-Net, and we propose a new ﬁtness

function that generalizes the previous one. It is still

based on the concept of community measure but it al-

lows to combine the semantic and the interaction cri-

terion by choosing their respective contribution to the

ﬁtness function according to thresholds. This concept

provides a generic solution of a partitioning commu-

nities that are semantically similar or/and interacting.

Moreover, a new genetic operation that is a speciﬁc

heuristic crossover operator adapted to our problem

is introduced. This heuristic crossover help the GA

to build communities with high values of similarities

or/and interaction between genes. The algorithm out-

puts the ﬁnal community by selectively exploring the

search space. Experiments on real datasets show the

ability of the proposed approach to correctly detect

communities having different sizes which are similar

and/or interact.

The contents of this paper are organized in six

main sections. The next section presents an overview

of the existing community detection algorithms. Sec-

tion 3 provides the problem deﬁnition. Section 4 de-

picts our main proposed algorithm for community de-

tection. In section 5, experimental results on real data

sets are presented and analyzed. Finally, section 6 re-

ports the conclusion.

2 COMMUNITY DETECTION

RELATED METHODS

Network community detection has an important role

in the networked data mining ﬁeld. Community de-

tection helps to discover latent patterns in networked

data and it affects the ultimate knowledge presenta-

tion (Cai et al., 2016).

The task for network community detection is to

divide the whole network into small parts or groups

which are also called communities. There is no uni-

form deﬁnition for community in the literature, but in

academic domain, a community (also called a cluster

or a module) is a group of nodes that are connected

densely inside the group but connected sparely with

the rest of the network. Radicchi et al. (Radicchi

et al., 2004) propose two deﬁnitions of community.

These deﬁnitions are based on the degree of a node

(or valency)

. In the ﬁrst deﬁnition, a community is a

subgraph in a strong sense: each node has more con-

nections within the community than the rest of the

graph. In the second deﬁnition, a community is a sub-

graph in a weak sense: the sum of all incident edges

in a node is greater than the sum of the out edges.

The problem of community detection has been re-

ceiving a lot of attention, in recent years, and many

different approaches have been proposed. The litera-

ture survey is divided into two categories: community

detection based on analytical approaches and those

The degree of a node is the number of edges incident

to the node.

ICSOFT 2020 - 15th International Conference on Software Technologies

296

based on evolutionary approaches (Pizzuti, 2018).

Analytical methods ﬁrstly split networks into sub-

groups according to their topological characteristics,

then the modularity assessment is carried out. The

modularity is deﬁned as the fraction of edges inside

communities minus the expected value of the fraction

of edges, if edges fall at random without regard to the

community structure. Values of modularity approach-

ing 1 indicate strong community structure. A well

known algorithm in this category is the one presented

by Girvan and Newman (Girvan and Newman, 2002;

Newman and Girvan, 2004). It is a divisive hierarchi-

cal clustering method based on an iterative removal

of edges from the network. The edge removal splits

the network in communities. The removed edges are

chosen by using betweenness measures (that repre-

sents the number of shortest paths between all vertex

pairs that run along the edge). The idea underlying the

edge betweenness comes from the observation that if

two communities are joined by a few inter-community

edges, then all the paths from vertices in one commu-

nity to vertices in the must pass through these edges.

Paths determine the betweenness score to compute for

the edges. By counting all the paths passing through

each edge, and removing the edge scoring the max-

imum value, the connections inside the network are

broken. This process is repeated, thus dividing the

network into smaller components until a stop crite-

rion is reached. The criterion adopted to stop the di-

vision is the modularity. In (Newman, 2004), the au-

thor presents an agglomerative hierarchical algorithm

that optimizes the concept of modularity. Thus the

algorithm computes the modularity of all the clusters

obtained by applying the hierarchical approach, and

returns as result the clusters having the highest value

of modularity.

Analytical algorithms do not reach the expected

successful results in community detection from com-

plex networks. Therefore, various evolutionary based

algorithms (EAs) have been proposed to provide dif-

ferent approaches to solve the community detection

problem (Atay et al., 2017). Many community eval-

uation criteria have been proposed and quantities

of methods that combine either single objective or

multiobjective EAs with community detection have

emerged. Most if not all of these methods share

the common feature that they model the community

detection problem as an optimization problem (Cai

et al., 2016). The single objective methods optimize a

single property, while the multiobjective approaches

simultaneously optimize competing objectives. The

most popular single evaluation criterion is the modu-

larity proposed by Newman and Girvan (Newman and

Girvan, 2004). Since 2002, several methods that di-

vide networks into clusters according to the modular-

ity criterion have been developed (Atay et al., 2017).

In (Tasgin and Bingol, 2006) and (Liu et al., 2007),

the authors presented an approach based on a GA to

optimize the network modularity introduced by New-

man and Girvan (Girvan and Newman, 2002). How-

ever, some studies have indicated that the optimiza-

tion of modularity has several drawbacks (Cai et al.,

2016). First, it has the resolution limitation, i.e., max-

imising the modularity can fail in ﬁnding communi-

ties smaller than a ﬁxed scale, even if these commu-

nities are well deﬁned. The scale depends on the total

size of the network and the interconnection degree of

the communities (Fortunato and Barth

elemy, 2007).

Second, maximizing the modularity is proved to be

NP-hard (Cai et al., 2016). These drawbacks can con-

stitute a weakness for all those methods whose objec-

tive is to optimize the modularity. To avoid the reso-

lution limitation of modularity, many multi-resolution

models have been developped (Cai et al., 2016). Piz-

zuti (Pizzuti, 2008) has proposed an algorithm named

GA-Net and has used a special assessment function

called community score that uses only graph topol-

ogy. This community score takes one parameter r

which is hard to tune because higher values of r help

to detect communities and low values of this paramter

return no communities. A modiﬁcation of the modu-

larity has been proposed in (Li et al., 2008) with the

concept of modularity density. The authors prove that

modularity density has a number of advantages with

respect to modularity, such as detecting communities

of different sizes.

Single objective optimization identiﬁes a single

best solution that gives insights on the graph orga-

nization. However, this solution could be biased to-

ward a particular structure inherent inside the crite-

rion to optimize (Cai et al., 2016). These methods

have obtained very good results on both artiﬁcial and

real-world networks (Pizzuti, 2018). The intuitive no-

tion of community that the number of edges inside

a community should be much higher than the num-

ber of edges connecting to the remaining nodes of

the graph, has two different objectives: 1) maximiz-

ing the internal connection links and 2) minimizing

the external connection links (Pizzuti, 2018). Thus,

on the basis of these objectives, many multi-objective

community models have been established. The ﬁrst

proposal framework to uncover community structure

has been presented by Pizzuti (Pizzuti, 2011; Pizzuti,

2009). In particular, the method introduces two objec-

tives: maximizing the community score proposed by

(Pizzuti, 2008) and minimizing the community ﬁtness

put forward by (Lancichinetti et al., 2009). Then, the

fast elitist non-dominated sorting genetic algorithm

Generic GA-PPI-Net: Generic Evolutionary Algorithm to Detect Semantic and Topological Biological Communities

297

(NSGA-II) proposed in (Deb et al., 2002) has been

applied. A variation of this method has been proposed

by Agrawal (Agrawal, 2011). The objectives to min-

imize are the modularity proposed by Newman and

Girvan (Girvan and Newman, 2002) and the commu-

nity score proposed by Pizzuti (Pizzuti, 2008). Sur-

veys on the selection of objective functions in multi-

objective community detection can be found in Shi et

al. (Shi et al., 2011; Shi et al., 2014).

Multi-objective evolutionary approaches, like the

single objective ones, are able to discover community

structures of quality comparable with, or even better

than, those obtained by analytical methods. Optimiz-

ing multiple objectives allows a simultaneous evalu-

ation of community structure from different perspec-

tives, then it is the user’s responsibility to choose a so-

lution (Cai et al., 2016). The choice of the objectives

to optimize should take into account the suggestions

given by Shi et al.(Shi et al., 2010), where a compari-

son of several objective functions in a multi-objective

framework has been performed (Pizzuti, 2018).

The use of evolutionary methods for community

detection presents a number of advantages (Pizzuti,

2018):

• During the search process, the communities’

number is generated automatically;

• Domain-speciﬁc knowledge can be incorporated

inside the method, such as biased initialization, or

speciﬁc variation operators instead of random, al-

lowing a more effective exploration of the state

space of possible solutions;

• The efﬁcient implementations of population-

based models can be realized to deal with large

size networks.

Most evolutionary approaches to detect communities

have been applied in social networks and have used

only graphical topology and no semantic similarity

between nodes (Pizzuti and Rombo, 2014). In this

paper we propose a generic evolutionary algorithm to

detect semantic or/and topological communities in bi-

ological networks. This new algorithm tries to ﬁnd the

best community by maximizing the concept of com-

munity measure. This measure is based on both the

graph topology (interaction) and the semantic simi-

larity between nodes. It is different to the community

score introduced by Pizzuti since it is not related to

the density introduced in (Pizzuti, 2008) and not re-

lated to the modularity of the sub-networks.

3 PROBLEM DEFINITION

The network of interactions between proteins is gen-

erally represented as an interaction graph G = (V,E)

where V is a set of objects, called nodes or vertices,

representing proteins and E is a set of links, called

edges, representing pairwise interactions. A commu-

nity (or cluster) in a network is a group of vertices

having a high density of edges within them, and a

lower density of edges between groups. In this work,

we design a community C as a group of genes or pro-

teins that are semantically similar and interact with

each other. A set of genes C = {G

,...,G

} is a

community if it respects the following property:

∀ G

∈ C, S(G

) ≥ ∇

or I(G

) ≥ ∇

(1)

Where:

• S(G

): the similarity value between two genes

and G

. To calculate the similarity between

two genes, we need to use a measure allowing to

compare sets of terms that annotate these genes

thus we can quantify the similarity between these

sets. In this work, we use the semantic similar-

ity measure GS2 (GO-based similarity of gene

sets) (Ruths et al., 2009). This measure aver-

ages the similarity contributed by each gene in

C. Each gene is compared with the remaining set

of genes by calculating how closely that gene fol-

lows the functionality distribution of the remain-

ing genes. The functionality distribution is rep-

resented by the distribution of ancestor GO terms

for each gene (Ruths et al., 2009).

• I(G

): the score of interaction between two

genes extracted from STRING Database (Mering

et al., 2003). This score explains the protein-

protein or the gene-gene associations known and

predicted according to different criteria in a bibli-

ographic reference.

• ∇

and ∇

are two thresholds. They are deﬁned

for both the semantic and the interaction criterion

respectively. Their values are ﬁxed according to

the recommendations of our biological expert.

• Each gene G can be annotated with a set of GO

(Gene Ontology) terms (Camon et al., 2003). We

use TP to denote the set of GO terms that annotate

a gene, this set is denoted by A(G). A(G).It con-

sists of an association between a gene and a GO

term. For example, the MEIKIN gene is identi-

ﬁed by ID: 728637 and annotated by the follow-

ing sets: ”GO: 0007060”, ”GO: 0010789”, ”GO:

0016321”, ”GO: 0045143”, ”GO: 0051754”.

More formally,

A(G) =

{

T P that annotate G /T P ∈ GO

}

(2)

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4 PROPOSED APPROACH

GAs have proved to be competitive alternative meth-

ods to traditional optimization and search techniques

and they have been applied to many problems in di-

verse research and application areas such neural nets

evolution, planning and scheduling, machine learning

and pattern recognition (Goldberg, 1989; Petrowski

and Ben-Hamida, 2017). Thus, it would be also suit-

able for solving the community detectionSIM group

problem.

We describe, in this section, the GA proposed in

this work as well as the genetic representation and the

variation operators that we propose.

The population is composed of individuals that are

the solutions of the problem. In our approach, an indi-

vidual is a set of proteins or genes that form a commu-

nity. A community may have different sizes. To eval-

uate a solution, we propose a ﬁtness function based

on a community measure. The latter uses the simi-

larity value and the interaction score of every pair of

genes making up the solution. Moreover, we modify

the steps of GA to satisfy the needs of our algorithm.

Thus, we propose a new heuristic crossover operator,

a new mutation operator and insert some additional

steps during the population initialization. The algo-

rithm works as follows:

Algorithm 1: General Algorithm of the Generic GA-PPI-

Net approach.

Require: algorithm parameters, problem instance

Ensure: best solution to the optimization problem

Begin

1: Initialize population

2: Evaluate the initial population

3: for i = 1 to max iteration do

4: Select parents for mating

5: for each pair of candidates in the set of parents

6: Generate an offspring through genetic oper-

ator - crossover and mutation - with respec-

tively a probability p

and p

7: Evaluate the ﬁtness of the offspring

8: Replace the worst existing individual in the

population by the obtained offspring

9: end for

10: end for

End

The various steps of the GA are described in the

following subsections.

4.1 Genetic Representation

A solution to our problem is a community of proteins

or genes. We represent it by a vector T. In this repre-

sentation, each individual stores: the size n of the cor-

responding community (= the number of proteins or

genes in the community) and the list of the n compo-

nents. Each component (gene or protein) is designed

by its name. A solution corresponds to an individual

in GA terms. Figure 1 illustrates the representation of

an individual adopted in our algorithm.

Figure 1: Example of individual representation designing a

community.

4.2 Population Initialization

In this work, the population is deﬁned as a two-

dimensional array of individuals. It represents some

potential solutions of the problem. In order to initial-

ize this population, we ﬁrst randomly recover com-

munities from the KEGG pathway database (Kane-

hisa and Goto, 2000). Then, we randomly create

the population with the recovered genes. The pop-

ulation is composed by individuals having different

sizes (Ben M’barek et al., 2019). Figure 2 presents an

example of an initial population with ﬁve individuals

having different sizes.

Figure 2: Example of an initial population.

4.3 Fitness Function

The ﬁtness function relates to the ability of the candi-

date to survive and reproduce. It takes as input a can-

didate solution to the problem and produces as output

a performance measure of the solution with respect

to the considered problem. The choice of the ﬁtness

function is a critical step for obtaining good solutions.

In the context of community detection, the most pop-

ular function is the modularity, originally introduced

by Girvan and Newman (Girvan and Newman, 2002).

In our work, we do not directly take into account the

modularity. Nevertheless, the topological propriety of

a community is taken into account through the inter-

action score between proteins or genes. Moreover,

Generic GA-PPI-Net: Generic Evolutionary Algorithm to Detect Semantic and Topological Biological Communities

299

the ﬁtness function is enriched with semantic infor-

mation. Indeed, we deﬁne a ﬁtness function based on

the computation of similarity value and the interaction

score of each pair of genes in a community C. Thus,

as a ﬁrst step for the ﬁtness computation, a similar-

ity matrix is computed using the GS2 measure (sec-

tion 3). Likewise, an interaction matrix is computed

designing the interaction score between each pair of

genes. The proposed ﬁtness function F is then deﬁned

as follows:

F =

∑

i6= j,i=1, j=1

i j

) (3)

i j

) =



0 if S(G

) ≤ ∇

or I(G

) ≤ ∇

S(G

) + I(G

) otherwise.

(4)

Where:

• S(G

) and I(G

) are, respectively, the sim-

ilarity and the interaction values of each pair of

genes (G

) introduced in section 3;

• ∇

and ∇

are two thresholds deﬁning respec-

tively the topology and the semantic levels. Their

values are ﬁxed in the beginning of the evolution.

This ﬁtness function generalizes the previous

method that we proposed in (Ben M’barek et al.,

2019). The used ﬁtness function in GA-PPI-Net was

based on the computation of the average similarity

value and the average interaction score of each two

genes existing in the community C. It is deﬁned as fol-

lows (Ben M’barek et al., 2018; Ben M’barek et al.,

2019):

F1(C) = W

AVGS(C) +W

AVGI(C) (5)

With:

• AVGS and AVGI being the average similarity

value and the average interaction value of genes

in C respectively.

• W

and W

: weights ∈ [0,1].

F1 corresponds to a particular case of F when choos-

ing speciﬁc values of the thresholds (∇

= ∇

= 0).

4.4 Selection

In this stage of a GA loop, individuals are selected

from the population to be parents which mate and re-

combine to create offspring for the next generation.

Selection is very crucial to the convergence rate of

the GA as good parents drive individuals to ﬁtter so-

lutions. The problem is how to select these individu-

als. In literature, there are many methods to select the

best individuals such as roulette wheel selection, tour-

nament selection, rank selection, elitism... (Goldberg

and Deb, 1991). For our problem we use the popular

tournament selection because it is highly efﬁcient and

easy to implement (Goldberg and Deb, 1991).

4.5 Genetic Operators

After the generation of an initial population, a GA car-

ries out the genetic operators to generate offspring.

Once a new population is created, the genetic pro-

cess is performed iteratively until an optimal result is

found or a maximum number of generations is met.

Crossover and mutation are two basic operators of

GA. The algorithm performance depends tightly on

the choice of these operators. Indeed, crossover and

mutation operators guide the convergence of the algo-

rithm towards a solution for the problem. Their goal

is to both exploit the best solutions and explore the

search space.

For this work, we propose the use of two types

of crossover. The ﬁrst one is the classic two-point

crossover. It is a generalization of the one-point

crossover. To apply this operator, two cross-points are

chosen randomly respecting the condition that their

positions do not exceed the longest parent size. Then,

the contents bracketed by these sites are exchanged

between two mated parents to get two new offspring.

A clean up phase is used in order to delete the redun-

dant gene in the created offspring. To better under-

stand this kind of crossover, a graphical illustration is

given in Figure 3. In this example, two sites are cho-

sen at random in position 1 and 4. Then two offspring

(Ch1,Ch2) are generated by exchanging the values of

the selected parents (P1,P2).

Figure 3: Example of a two points crossover operator.

The second crossover operator is a new heuristic

crossover. It is speciﬁc to our problem. The main

purpose of this operator is to create an offspring with

higher value of similarity or interaction or both sim-

ilarity and interaction. It is applied according to the

Algorithm 2.

The application of this heuristic crossover

helps the GA to build communities with high

values of similarities and interaction between

genes than the imposed thresholds. Figure 4

presents a graphical illustration to understand

the proposed heuristic crossover. Two individu-

als (P1,P2) are chosen randomly from the parent

population. This operator is usually applied with

ICSOFT 2020 - 15th International Conference on Software Technologies

300

Algorithm 2: Heuristic Crossover Algorithm.

1: Choose randomly two parents (P1,P2) from the

the parent population;

2: Merge the values of two parents (P1,P2) by re-

moving the redundant content (genes) to obtain

one individual P;

3: for each two genes G

and G

∈ P do

4: if i 6= j then

5: Compute the similarity Sim(G

);

6: Compute the interaction Interaction(G

);

7: end if

8: end for

9: for each two genes G

and G

∈ P do

10: if Sim(G

) ≥ ∇

then

11: Add the genes to the ﬁrst offspring Ch1;

12: end if

13: if Interaction (G

) ≥ ∇

then

14: Add the genes to the second offspring Ch2;

15: end if

16: end for

17: Remove the redundant content (genes) to the ob-

tained offspring Ch1 and Ch2;

a high probability (p

) (Pizzuti, 2018). Then, the val-

ues of two parents (P1,P2) are merged by removing

the redundant content (genes) to obtain one individ-

ual P. After that two offspring (Ch1,Ch2) are created

according to the following conditions:

1. ∀i 6= j : G

∈ P, if Sim(G

) ≥ ∇

then add

the gene G

to the ﬁrst offspring Ch1;

2. ∀i 6= j : G

∈ P, if Interaction (G

) ≥ ∇

then else add the gene G

to the second offspring

Ch2;

The mutation is an operator that acts in a rarer

fashion and in an unpredicted form to modify the

genes of the individual, promoting the diversiﬁcation

of the population. However, the mutation must not be

too destructive and a speed bump for the process of

ﬁnding an optimal solution (Pizzuti, 2018). For these

purpose, we propose for the present problem a new

speciﬁc mutation operator called Optimized Commu-

nity Mutation (OCM). Mutation may be deﬁned as a

small random tweak in the individual, to get a new so-

lution. It is used to maintain and introduce diversity in

the population and is usually applied with a low prob-

ability (p

). If the probability is very high, the GA

gets reduced to a random search (Pizzuti, 2018). We

present now the used mutation operator already de-

ﬁned in our previous work (Ben M’barek et al., 2018).

Its goal is to maximize the chance of creating a better

solution than the original one. This operator can inte-

grate a new gene in order to replace a gene having a

poor quality or to enlarge the size of the community.

To mutate a solution C, the mutation operator alters

only one gene at a time and uses a score function, de-

noted GS, applied to each gene in C. This score helps

us to detect the gene having the best score in a com-

munity as well as the gene having the worst score. It

is equal to the sum of the average similarity and the

average interaction score of a gene in a community

(Ben M’barek et al., 2018). It is deﬁned as follows:

AVGSim(G) =

∑

i=1

S(G,Gi)/n (6)

AVGInteraction(G) =

∑

i=1

I(G, G

)/n (7)

GS(G) = AV GSim(G) + AV GInteraction(G) (8)

Where:

• S(G, G

): The similarity value between a gene G

and the gene G

in the community C;

• I(G, G

): The interaction score of a gene G com-

pared to the gene G

in the community C.

• n: size of an individual (community).

The OCM mutation operator is applied according

to the following steps (Ben M’barek et al., 2019) pre-

sented in algorithm 3.

Algorithm 3: OCM algorithm.

1: Select in a solution C a gene having the highest

score GS that will be called ”bestGene”;

2: Randomly search a gene G

from the ”interac-

tion” table with which the ”bestGene” interacts

and G

/∈ C;

3: Get the gene having the lowest score GS in C, it

will be called ”worstGene”;

4: Fix a threshold θ (i.e θ = 0.5);

5: if GS(”worstGene”) ≤ θ then

6: replace the ”worstGene” by the gene G’ se-

lected in the second step;

7: else

8: Insert into the end position of the solution the

gene G’ selected in the second step and update

the size.

9: end if

5 EXPERIMENTAL RESULTS

In this section, we study the effectiveness of our

approach on real data sets (Pathways selected from

KEGG Pathway database). A set of preliminary tests

have been carried out to tune the GA parameters: pop-

ulation size, crossover and mutation rate and maxi-

mum number of generations. The retained values are

summarized in Table 1.

Generic GA-PPI-Net: Generic Evolutionary Algorithm to Detect Semantic and Topological Biological Communities

301

Figure 4: Example of an heuristic crossover operator (∇

= 0.7 and ∇

= 0.4).

Table 1: GA parameters.

Parameters Values

Population size 30

Generation number 100

Crossover rate 0.8

Mutation rate 0.01

Individual’size in the initial population [5,40]

∇

≥ 0.5

∇

≥ 0.4

A community of genes is accepted if the value of

the thresholds ∇

and ∇

are sufﬁcient. The role of

these two thresholds is to vary the weight of the qual-

ity of a community. The ∇

allows to quantify the

similarity value. It is is set according to the GO an-

notations among a set of genes by averaging the con-

tribution of each gene’s GO terms and their ances-

tor terms with respect to the GO vocabulary graph

(Ben M’barek et al., 2018). If it is greater than or

equal to 0.5 then these two genes are similar, else they

are not (Ben M’barek et al., 2018). The ∇

refers to

the interaction value which deﬁnes the number of ci-

tation of this interaction in the literature. If this value

is greater than or equal to 0.4 then these two genes

have a strong interaction. The values of ∇

and ∇

are proposed by the biological expert and modiﬁed as

needed.

To check the capability of our approach to suc-

cessfully detect the communities of a network, we

pick a set of proteins or genes randomly from the

reference database KEGG pathway. More precisely,

our approach has been tested with ﬁve datasets pro-

posed by our biological expert. Their names and their

corresponding genes’ numbers are described in Ta-

ble 2. These datasets correspond to existing commu-

values proposed by the biological expert and modiﬁed

as needed

Table 2: The used datasets.

Datasets Genes’Number

Apoptosis

B cell receptor signalling

Purine metabolism

159

Rna degradation

159

Oocyte meiosis

114

Total 595

nities presented in KEGG pathway database.

The ﬁrst evaluation consists to verify how the pro-

posed method is likely to ﬁnd gene communities hav-

ing high similarity or/and high interaction.

The obtained communities are analyzed and com-

pared for a better comprehension of the topological

(interaction between genes) or/and the semantic sim-

ilarity measures. We performed tests with different

values of the proposed thresholds ∇

and ∇

of the

ﬁtness function to determine communities of genes.

These values were proposed by the biological expert.

Actually, the tests showed that it was possible to de-

tect three types of existing communities of genes or

proteins having high interaction and/or high similar-

ity between their genes:

1. ∇

≥ 0.5 and ∇

= 0: detect similarity based

group of genes.

2. ∇

≥ 0.5 and ∇

≥ 0.4: detect similarity and in-

https://www.genome.jp/dbget-bin/www bget?

pathway:hsa04210

https://www.genome.jp/dbget-bin/www bget?

pathway:hsa04662

https://www.genome.jp/dbget-bin/www bget?

pathway:hsa00230

https://www.genome.jp/dbget-bin/www bget?

pathway:hsa03018

https://www.genome.jp/dbget-bin/www bget?

pathway:hsa04114

ICSOFT 2020 - 15th International Conference on Software Technologies

302

teraction based genes communities.

3. ∇

< 0.5 and ∇

≥ 0.4: detect interaction based

group of genes.

To determine these three types of communities, we re-

alized different experiments. We apply our approach

with proteins or genes chosen randomly from the ﬁve

proposed datasets (see Table 2). We vary the values

of the thresholds ∇

and ∇

in the range [0..1] and we

retained each time the best community. For the ﬁrst

runs, we set the threshold values such that ∇

≥ 0.5

and ∇

≥ 0.4. We use ∇

= 0.6 and ∇

= 0.4. The

preliminary results showed clearly that our proposed

GA is able to detect similarity and interaction based

genes communities. For instance, Figure 5 clearly

shows a solution in these category of experiments

to detect communities of genes that are semantically

similar or/and interact (where the edge value I ≥ 0.4

and S ≥ 0.5).

Then, we test our GA with ∇

≥ 0.5 and ∇

= 0.

We turn our algorithm with values: ∇

= 0.6 and

∇

= 0. And, we obtain as result a group of genes

that are semantically similar and do not interact with

each other. One solution in these category of experi-

ments is shown in Figure 6 which represents a group

of genes having size 9.

For the last set of experiments, we set the thresh-

olds such as ∇

< 0.5 and ∇

≥ 0.4. We use ∇

= 0.3

and ∇

= 0.4. The obtained results show the capabil-

ity of the proposed GA to build an interaction based

group. Figure 7 illustrates an example of a detected

group of genes.

To conclude, the main goal by introducing thresh-

olds in the ﬁtness function is to allow the GA to detect

three types of genes or proteins’ communities:

• a group of genes with high similarity (if ∇

≥

0.5).

• a group of genes with high interaction (if ∇

≥

0.4).

• a community of genes with high interaction and

high similarity (if ∇

≥ 0.5 and ∇

≥ 0.4).

To interpret biologically the obtained communities,

our biological expert proposed to evaluate them by

checking if they exist in KEGG or other biological

pathway databases. Each new community found by

our generic GA-PPI-Net is presented to the DAVID

tools (Database for Annotation Visualization and In-

tegrated Discovery) (Sherman et al., 2007), which

compares the founded community, denoted by Rnew,

with others in different databases and gives the per-

centage of Rnew genes that belong to the existing

communities in those databases. DAVID bioinfor-

matics resources consist of an integrated biological

knowledge-base and analytic tools that aim at sys-

tematically extracting biological meaning from large

gene/protein lists. It is the most popular functional an-

notation programs used by biologists (Sherman et al.,

2007). It takes a list of genes as input and exploits

the functional annotations available on these genes in

a public database such as, KEGG Pathways in order

to ﬁnd common functions that are sufﬁciently speciﬁc

to these genes.

For each types of genes or proteins’ communities,

we run our approach 20 times with proteins or genes

chosen randomly from the ﬁve proposed datasets in

Table 2. And, we retained each time the best com-

munity. Thus, we have 20 best communities for

each type. We evaluate these obtained communi-

ties by checking if they exist in biological pathway

databases. The biological databases used to evaluate

our results are KEGG, Biocarta, Reactome, BBID and

EC Number. The results of this evaluation are shown

in Table 3 column Ben M’barek et al. 2020.

Our new approach is parameterized according to

the importance affected to each measure criterion (se-

mantic similarity measure and interaction measure).

This parameters allows us to detect communities ac-

cording to either both criteria, or only one crite-

rion. The results presented in Table 3 column Ben

M’barek et al. 2020 show that the new communi-

ties obtained by our algorithm correspond to some

”parts” of real networks existing in other biological

pathway databases, and in some cases to a complete

network (percentage 100%). The Generic GA-PPI-

Net achieves the highest percentage 80%, 90% and

100% when the ﬁtness function is based on both sim-

ilarity and interaction values. And it achieves the per-

centage 90% and 100% when the ﬁtness function is

based on semantic similarity criterion or interaction

criterion respectively.

These results are considered very satisfactory by

the biology expert. They constitute an initial valida-

tion of our algorithm and show the relevance of the

used ﬁtness function. These tests should be supple-

mented on a larger scale with other datasets and dif-

ferent communities.

Moreover, we compare the results obtained by our

new algorithm with the one proposed in (Ben M’barek

et al., 2019). We design these approaches as Ben

M’barek et al. 2019 and Ben M’barek et al. 2020

respectively. A thorough comparison is not easy be-

cause the obtained communities for both propositions

haven’t the same sizes and the same constitution.

Hence, the same datasets proposed by the biological

expert in Table 2 and the same GA parameters were

used for both approaches. The two algorithms were

executed 20 times. We also used the DAVID tools

Generic GA-PPI-Net: Generic Evolutionary Algorithm to Detect Semantic and Topological Biological Communities

303

Figure 5: Example of a detected community of genes having high similarity and interaction scores (∇

≥ 0.5 and ∇

≥ 0.4).

Figure 6: Example of a detected similarity based groups of genes (∇

≥ 0.5 and ∇

= 0).

to estimate the recovery rate of the found commu-

nities with existing communities in different biolog-

ical databases. Table 3 column Ben M’barek et al.

2019 illustrates the results of the approach presented

in (Ben M’barek et al., 2019).

Table 3 shows how the present approach (Ben

M’barek et al. 2020) has additional abilities, accord-

ing to the use of thresholds in the ﬁtness function, to

detect communities of genes based only on semantic

similarity or interaction criterion. Otherwise, accord-

ing to the results in the last two columns in Table 3,

the ability to detect communities based on both crite-

Figure 7: Example of a detected interaction based groups of

genes (∇

< 0.5 and ∇

≥ 0.4).

ICSOFT 2020 - 15th International Conference on Software Technologies

304

Table 3: Evaluation of the obtained communities. Comparison with Ben M’barek et al. 2019 approach.

Pathway DBs Ben M’barek et al. 2020 Ben M’barek et al. 2019

SIM groups INT groups SIM & INT communities

%Min %Max %Min %Max %Min %Max %Min %Max

BBIB 20% 45% 15% 50% 20% 90% 25% 50%

Biocarta 10% 70% 20 % 50% 20% 100% 20% 66%

EC Number 10% 90% 10% 60% 30% 100% 10% 100%

KEGG 9% 78% 10% 62% 11% 100% 15% 100%

Reactome 20% 75% 15% 100% 10% 80% 14% 100%

ria of the new GA is similar or better than the one of

Ben M’barek 2019 GA. Indeed, our new GA achieves

a higher percentage than Ben M’barek et al.2019 ap-

proach in 4 pathway databases: Kegg, Biocarta, BBIB

and Ec Number when the ﬁtness function is based on

both similarity and interaction values. In other re-

spects, the Ben M’barek et al. 2019 approach has a

higher result than the Ben M’barek et al. 2020 ap-

proach in the Reactome pathway. Nevertheless, the

new GA keeps satisfactory percentage for the three

types of detected communities (75% for SIM groups

column, 100% for INT groups column and 80% for

SIM & INT communities column). The obtained per-

centage values corresponds to a complete communi-

ties or to some ”parts” of the real communities.

To conclude, the obtained results show the ability

of the GA proposed in this paper to effectively deal

with community detection in networks. Moreover,

the new GA allows to detect communities of genes

or proteins having different size according to the use

of thresholds in the ﬁtness function. This thresholds

allows us to detect communities according to either

both criteria, or only one criterion. Thus, we obtain

three types of genes or proteins’ communities, which

improve that this approach is a generic approach of

Ben M’barek et al. 2019 approach. Further exten-

sions experiments will be carried out to detect com-

munities with larger size and identify new communi-

ties not yet known in the public biological databases.

6 CONCLUSIONS

In this paper, we have proposed a generic approach

based on GA to detect communities of interacting

genes or proteins. This approach is a generalization of

a previous work. It introduces the concept of commu-

nity measure and searches for an optimal partitioning

of the network by maximizing this measure. Our con-

tribution in this paper is threefold. First, we apply GA

to community detection in PPI networks. Second, we

modify the previous proposed ﬁtness function to al-

low our GA to detect communities of genes that are

semantically similar and/or interacting. Third, we de-

ﬁne a speciﬁc heuristic crossover operator adapted to

the considered biological problem. Dense communi-

ties existing in the network are obtained at the end

of the evolution by selectively exploring the search

space, without the need to know in advance the com-

munity size. The experimental results showed the

ability of the GA approach to correctly detect commu-

nities having different sizes which are semantically

similar and/or interacting. Future research will aim

at extending the proposed ﬁtness function by adding

the modularity value and applying a multi-objective

optimization to improve the quality of the results.

ACKNOWLEDGEMENTS

We would like to show our gratitude to Dr. Walid

BEDHIAFI (Laboratoire de G

entique Immunologie et

Pathologies Humaines, Universit

e de Tunis El Manar)

for assistance to comprehend the biological ﬁelds and

for the interpretation of the results.

REFERENCES

Agrawal, R. (2011). Bi-objective community detection

(bocd) in networks using genetic algorithm. In In-

ter Conf on Contemporary Computing, pages 5–15.

Springer.

Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D.,

Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K.,

Dwight, S. S., Eppig, J. T., Harris, M. A., Hill, D. P.,

Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese,

J. C., Richardson, J. E., Ringwald, M., Rubin, G. M.,

and Sherlock, G. (2000). Gene ontology: tool for

the uniﬁcation of biology. The Gene Ontology Con-

sortium. Nat. Genet., 25(1):25–29.

Atay, Y., Koc, I., Babaoglu, I., and Kodaz, H. (2017). Com-

munity detection from biological and social networks:

A comparative analysis of metaheuristic algorithms.

Applied Soft Computing, 50:194–211.

Ben M’barek, M., Borgi, A., Bedhiaﬁ, W., and Ben Hmida,

S. (2018). Genetic Algorithm for Community Detec-

tion in Biological Networks. Proc Computer Science,

126:195–204. Knowledge-Based and Intelligent In-

Generic GA-PPI-Net: Generic Evolutionary Algorithm to Detect Semantic and Topological Biological Communities

305

formation & Engineering Systems: Proc of the 22nd

Inter Conf, KES-2018, Belgrade, Serbia.

Ben M’barek, M., Borgi, A., Ben Hmida, S., and Rukoz,

M. (2019). Genetic algorithm to detect different

sizes’ communities from protein-protein interaction

networks. In Proc of the 14th Inter Conf on Software

Technologies - Volume 1: ICSOFT,, pages 359–370.

INSTICC, SciTePress.

Cai, Q., Ma, L., Gong, M., and Tian, D. (2016). A survey on

network community detection based on evolutionary

computation. Int. J. Bio-Inspired Comput., 8(2):84–

98.

Camon, E., Magrane, M., Barrell, D., Binns, D., Fleis-

chmann, W., Kersey, P., Mulder, N., Oinn, T., Maslen,

J., Cox, A., and Apweiler, R. (2003). The Gene

Ontology Annotation (GOA) Project: Implementa-

tion of GO in SWISS-PROT, TrEMBL, and InterPro.

Genome Res, 13(4):662–672.

Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002).

A fast and elitist multiobjective genetic algorithm:

Nsga-ii. IEEE transactions on evolutionary compu-

tation, 6(2):182–197.

Fortunato, S. and Barth

elemy, M. (2007). Resolution limit

in community detection. PNAS, 104(1):36–41.

Fortunato, S. and Hric, D. (2016). Community detection in

networks: A user guide. Physics Reports, 659:1–44.

arXiv: 1608.00163.

Girvan, M. and Newman, M. E. J. (2002). Community

structure in social and biological networks. Proc. Natl.

Acad. Sci. U.S.A., 99(12):7821–7826.

Goldberg, D. E. (1989). Genetic Algorithms in Search, Op-

timization and Machine Learning. Addison-Wesley

Longman Publishing Co., Inc., Boston, MA, USA, 1st

edition.

Goldberg, D. E. and Deb, K. (1991). A comparative anal-

ysis of selection schemes used in genetic algorithms.

In Foundations of Genetic Algorithms, pages 69–93.

Morgan Kaufmann.

Jiao, X., Sherman, B. T., Huang, D. W., Stephens, R.,

Baseler, M. W., Lane, H. C., and Lempicki, R. A.

(2012). DAVID-WS: a stateful web service to fa-

cilitate gene/protein list analysis. Bioinformatics,

28(13):1805–1806.

Kanehisa, M. and Goto, S. (2000). KEGG: kyoto ency-

clopedia of genes and genomes. Nucleic Acids Res.,

28(1):27–30.

Lancichinetti, A., Fortunato, S., and Kertesz, J. (2009).

Detecting the overlapping and hierarchical commu-

nity structure in complex networks. New journal of

physics, 11(3):033015.

Li, Z., Zhang, S., Wang, R.-S., Zhang, X.-S., and Chen, L.

(2008). Quantitative function for community detec-

tion. Physical review E, 77(3):036109.

Liu, X., Li, D., Wang, S., and Tao, Z. (2007). Effective al-

gorithm for detecting community structure in complex

networks based on ga and clustering. In Inter Conf on

Computational Science, pages 657–664. Springer.

Mering, C. v., Huynen, M., Jaeggi, D., Schmidt, S., Bork, P.,

and Snel, B. (2003). STRING: a database of predicted

functional associations between proteins. Nucl. Acids

Res., 31(1):258–261.

Newman, M. E. J. (2004). Fast algorithm for detecting

community structure in networks. Physical Review E,

69(6). arXiv: cond-mat/0309508.

Newman, M. E. J. and Girvan, M. (2004). Finding and eval-

uating community structure in networks. Physical Re-

view E, 69(2). arXiv: cond-mat/0308217.

Petrowski, A. and Ben-Hamida, S. (2017). Evolutionary

Algorithms. John Wiley & Sons. Google-Books-ID:

fvRRCgAAQBAJ.

Pizzuti, C. (2008). Ga-net: A genetic algorithm for commu-

nity detection in social networks. In Inter conf on par-

allel problem solving from nature, pages 1081–1090.

Springer.

Pizzuti, C. (2009). A multi-objective genetic algorithm for

community detection in networks. In 2009 21st IEEE

Inter Conf on Tools with Artiﬁcial Intelligence, pages

379–386. IEEE.

Pizzuti, C. (2011). A multiobjective genetic algorithm to

ﬁnd communities in complex networks. IEEE Trans-

actions on Evolutionary Computation, 16(3):418–

430.

Pizzuti, C. (2018). Evolutionary Computation for Commu-

nity Detection in Networks: A Review. IEEE Transac-

tions on Evolutionary Computation, 22(3):464–483.

Pizzuti, C. and Rombo, S. E. (2014). Algorithms and

tools for protein–protein interaction networks cluster-

ing, with a special focus on population-based stochas-

tic methods. Bioinformatics, 30(10):1343–1352.

Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., and

Parisi, D. (2004). Deﬁning and identifying communi-

ties in networks. PNAS, 101(9):2658–2663.

Ruths, T., Ruths, D., and Nakhleh, L. (2009). GS2: an ef-

ﬁciently computable measure of GO-based similarity

of gene sets. Bioinformatics, 25(9):1178–1184.

Sherman, B. T., Huang, D. W., Tan, Q., Guo, Y., Bour, S.,

Liu, D., Stephens, R., Baseler, M. W., Lane, H. C.,

and Lempicki, R. A. (2007). DAVID Knowledge-

base: a gene-centered database integrating heteroge-

neous gene annotation resources to facilitate high-

throughput gene functional analysis. BMC Bioinfor-

matics, 8:426.

Shi, C., Yu, P. S., Cai, Y., Yan, Z., and Wu, B. (2011).

On selection of objective functions in multi-objective

community detection. In Proc of the 20th ACM in-

ternational conference on Information and knowledge

management, pages 2301–2304. ACM.

Shi, C., Yu, P. S., Yan, Z., Huang, Y., and Wang, B. (2014).

Comparison and selection of objective functions in

multiobjective community detection. Computational

Intelligence, 30(3):562–582.

Shi, C., Zhong, C., Yan, Z., Cai, Y., and Wu, B. (2010). A

multi-objective approach for community detection in

complex network. In IEEE Congress on Evolutionary

Computation, pages 1–8. IEEE.

Tasgin, M. and Bingol, H. (2006). Community Detec-

tion in Complex Networks using Genetic Algorithm.

arXiv:cond-mat/0604419. arXiv: cond-mat/0604419.

Tasgin, M., Herdagdelen, A., and Bingol, H. (2007). Com-

munity Detection in Complex Networks Using Ge-

netic Algorithms. arXiv:0711.0491 [physics]. arXiv:

0711.0491.

ICSOFT 2020 - 15th International Conference on Software Technologies

306