arXiv:1507.02870v1 [] 10 Jul 2015

Analysis of Microarray Data using Artificial
Intelligence Based Techniques
Khalid Raza
Department of Computer Science
Jamia Millia Islamia (Central University), New Delhi, India
Email: kraza@jmi.ac.in
June 30, 2015

Abstract
Microarray is one of the essential technologies used by the biologist to measure genome-wide expression levels of genes in a particular
organism under some particular conditions or stimuli. As microarrays
technologies have become more prevalent, the challenges of analyzing
these data for getting better insight about biological processes have
essentially increased. Due to availability of artificial intelligence based
sophisticated computational techniques, such as artificial neural networks, fuzzy logic, genetic algorithms, and many other nature-inspired
algorithms, it is possible to analyse microarray gene expression data
in more better way. Here, we reviewed artificial intelligence based
techniques for the analysis of microarray gene expression data. Further, challenges in the field and future work direction have also been
suggested.

1

Introduction

The bioinformatics is an interdisciplinary area of study where one of the objectives is to deal with the analysis and interpretation of large sets of data
generated from various large-scale biological experiments. The example of
one such large-scale biological experiment is measuring the expression levels
of tens of thousands of genes simultaneously under some environmental condition. Microarray is one of the essential technologies used by the biologist
to measure genome-wide expression levels of genes in a particular organism.
As microarrays technologies have become more prevalent, the challenges

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associated with collecting, managing, and analyzing the data from each experiment have essentially increased. Robust laboratory protocols, improved
understanding of the complex experimental design and falling prices of commercial platforms, all these have combined to drive the field to more complex
experiments, generating huge amounts of data (Brazma and Vilo, 2000).
With the help of measured transcription levels of genes under different
biological conditions (e.g. at various developmental stages and in different
tissues), biologists are able to develop gene expression profiles that differentiate the functionality of each gene in the genome. The gene expression
profiles are organized in the form of a matrix, where rows represents genes,
columns represents samples/replicas, and each cell of the matrix contains
a numeric value representing the expression level of a gene in a particular sample. Generally, such as table is called gene expression matrix. If
over expression of certain genes is correlated with a certain disease then
researchers can discover what are other conditions affecting expression-level
of these genes. Also, what are the other set of genes having similar expression profiles pattern. Hence, suitable compounds (potential drugs) can
investigated that can lower the expression level of these overexpressed genes
(Babu, 2004).
Many sophisticated statistical and computational tools have been developed to help biologists for the analysis of gene expression data and to identify novel targets from their experimental data (Deng et al., 2009; Debouck
and Goodfellow, 1999). Among these techniques, clustering and statistical
methods are most commonly used data analysis methods. Clustering generally groups the gene expression data with similar expression pattern, i.e.
co-expressed genes. However, clustering approach suffers from several drawbacks (Bassett et al., 1999). The statistical methods help to analysis gene
expression data and infer relationships between genes. However, it fails to
provide complex regulatory relations among genes.
The chapter is organized as follows. Section 2 describes the background
of Microarray experiments and data generation. Section 3 covers the applications of Microarrays and Section 4 describes artificial intelligence based
techniques, and reviews its application in the analysis of Microarray data.
Section 5 summarizes the chapter and presents research challenges and future work directions.

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2

Microarray Technology

With the help of Microarray technology, one can measure the expression
level of all genes in a genome simultaneously. By measuring and comparing
the expression level of genes in an unhealthy versus healthy cell, it would be
possible to identify genes which are responsible for various diseases. Due to
unprecedented amount of large biological data generated out of microarray
experiments, researchs focus has shifted from the generation of data to the
analysis and presentation of data in the most efficient manner (Hood, 2003;
Kitano, 2002a,b). With the help of these technologies, researchers can find
out answer to some of the challenging questions like;
(i) What are the functions of different genes?
(ii) In what cellular processes do these genes participate?
(iii) How genes are regulated?
(iv) How genes and its products (proteins) do interact, and what are these
interaction networks?
(v) How expression level of genes differs in different cell types and states?
(vi) How expressions of genes are affected by various disease or drug treatments?
Microarrays are frequently used in biomedical research to tackle a number of problems, including classification of tumors, or gene expression response to different stress conditions. A central and frequently asked question
in microarray is the identification of differentially expressed genes (DEGs).
The DEGs are those genes whose expression levels are associated with a
response or covariate of interest (Dudoit et al., 2002). The covariates can be
either polytomous (for instance, treatment/control status, cell type, drug
type) or continuous (for instance, drug doses), and the responses can be,
for instance, censored survival times or any other clinical outcomes (Dudoit
et al., 2002; Lee et al., 2012). Scientists from different disciplines such as
biology, statistics, computer science, mathematics, bioinformatics, etc. are
working in this area to identify some new insight from DNA microarray data
such as identification of differentially expressed genes, classification between
cancerous and non-cancerous genes, identification of potential genes for drug
target, identification of gene function, and so on. In the following section,
various steps involved in Microarray experiments have been discussed.
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2.1

Experimental Setup

Basically microarray is a solid base having grid of spots where genetic material of known sequence is arranged systematically. It is mostly made up
of glass on which single stranded DNA molecules are attached at fixed positions. The size of the arrays can vary from microscope slide to square
silicon chips. On an array, there can be thousands of spots and each spots
contain number of identical DNA molecules. The microarray fabrication can
be done in two ways: i) cDNA and ii) oligonucleotide. The steps involved
in microarray experiments are shown in Fig. 1.

Figure 1: Step involved in microarray experiments

2.1.1

Preparation of probe DNA

For the study of large-scale expression, a specific DNA sequence is needed for
all genes whose expression values are to be measured. The selection of probe
is done on the basis of resources available for obtaining the representation
of the genes under studies. The simplest way is to amplify every known
ORF in the genome and use it as a probe. PCR is used for the amplification
purpose and allows multiplication of DNA fragments by millions in just few
hours. ESTs may be used to identify distinct mRNA transcripts.
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2.1.2

Printing array

In cDNA array, arrays are mostly printed on Poly-L-lysine coated glass
microscope slide using arraying robot. During the arraying operation, a
large number of slides are placed on and secured to a platter. The samples of
DNA are placed in microliter plates on the stand. The reservoir slot of each
tip is filled with 1 liter of DNA solutions. The tips are then lightly tapped
at identical positions on each slide leaving a small drop of DNA solution on
the poly-L-lysine coated slide. In Oligonucleotide array, oligos are printed
at the spots instead of cDNA. Same robotics can be applied to manufacture
both types of arrays. However, the preparation of oligonucleotide array is
quite different. During fabrication of array, the probes are synthesized on
the chip using photolithography.
2.1.3

Post processing of slides

This step consists of Rehydration and Blocking. The spots on the microarray
are rehydrated to distribute DNA more evenly. In the blocking process, free
reactive groups on the slide surface are modified to minimize their ability
to bind to labeled target DNA. If these groups are not blocked, the labeled
DNA target can bind to the surface of the slide.
2.1.4

Preparation of target

In this step, isolate mRNA from the samples and purify it. Since, mRNA
degrades very fast; hence it is reverse-transcribed into more stable cDNA.
2.1.5

Hybridization

In hybridization process, a single stranded DNA molecule is bound to another single strand DNA molecule with a precisely matching sequence. After
hybridization process, the microarray properly washed to eliminate any excess labeled sample and finally dried using a centrifuge. Sometimes two types
of target mRNA samples are simultaneously hybridized on the array, called
two channel microarray experiment. In that case, two types of molecules
are added to targets and uses fluorescent dyes like Cy3 and Cy5, which can
be separated spectrally. The Cy3 is green and Cy5 is red when excited by
laser light at specific wavelength.

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2.1.6

Slide imaging

Under this step, the microarray is scanned to measure the fluorescent signal
emitted at every spot that determine the amount of labeled sample bound
to each spot. The laser scanning confocal microscope is used for this purpose. For single channel array, the array is scanned once but for two-channel
experiment, it is scanned in two phases. In the obtained image, the intensity of each spot is proportional to the amount of mRNA from the sample,
matching cDNA sequence of given pot. A gene expressed in a sample labeled
with red dye and not expressed in the other sample will produce a red spot
and vice versa. A gene expressed in both the samples will produced equal
amount of red and green intensities and a spot is given yellow (Fig. 2).

Figure 2: Preparation of cDNA probe and microarray

2.2

Quantification of Images

The images generated by scanner of microarray are the raw data. There is
one image per array for a single channel microarray, while there are two images per array for two channel microarrays. The image intensity is scanned
by detector at a high spatial resolution, where every probe spots are repre6

sented by several pixels. The intensity values of each probe are identified
and these intensities are quantified to numeric values. Image quantification
process involves following steps:
(i) Identification of position of sports on the array
(ii) For every spot on the array, pixel identification on the image
(iii) For every spot on the array, identifying pixels so that it may be used
for background calculation
(iv) Computation of numeric value for the intensity of the spot, intensity
of background image and quality control information.
There are several methods for segmentation and quantification which are
available in software packages but they differ in their robustness. To a microarray project, quantification of image involves the transition of workflow
from wet-lab procedure to computational (dry-lab). During the computation
of numeric information at the microarray spot, image processing software
provides a number of measures such as mean, median and standard deviation of signal and background, along with diameter and number of pixels.
Among these measures, the most important measure is hybridization intensity for each spot that can be either mean or median of the pixel intensities.
The second important information is signal standard deviation that helps
in computation of coefficient of variation for spot as well as for the background. Once the spotted image and other statistics are computed then it
is suggested that quality of the array and individual spots on the array are
assessed because sometimes array may have a few spots as defected.
The obtained microarray gene expression data is presented in a tabular
form, called gene expression matrix, as shown in Fig. 3. The first column
specifies the identity of genes and first rows represents samples (replicas),
condition or time-series observations. The element of the matrix gives expression values of gens under various conditions or samples.

2.3

Data Preprocessing and Normalization

Once the spotted images have been quantified to generate datasets, these
datasets should be preprocessed before its analysis and interpretation. In
this step, meaningful characteristics are extracted or enhanced and prepare
the dataset for its analysis and interpretation. In data preprocessing step,
generally two issues are addressed: i) to adjust background intensities, and
ii) to transform data into scale suitable for analysis and interpretation. A
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Figure 3: Preparation of cDNA probe and microarray
simple example of preprocessing microarray data is taking the Log of the raw
intensity values. The main purpose of normalization is to ensure that variation in the expression values are because of biological differences between
the mRNA samples and not because of experimental artifacts.
The adjustment of background intensities are needed because despite
of washing done after hybridization in microarrays, there are chances of
genes annealing in the background of the spot and during scanning time,
it may give rise to background intensity. Another issue in the gene expression data which need to be addressed is the difference between the data
generated by two microarray technologies (cDNA and oligonucleotide microarrays). The cDNA reports differences in gene expression, while oligonucleotide microarray report absolute expression values (Butte, 2002). Hence,
same normalization techniques may not be applied to these different microarray technologies. In a given experiment, most genes do not changes
their expression levels and if equal numbers of genes are upregulated and
downregulated, then differential expression measurements might found to
be normally distributed.
2.3.1

Normalization for single channel experiment

Suppose that one need to find out differentially expressed genes (DEGs)
under various experimental conditions, such as normal sample against cancerous sample, control tissue versus treatment, etc. in a single channel
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experiment. It is generally expected that gene expression values in both
the conditions are more or less similar but it is seldom found in the reality.
This variation is because of many factors including different arrays are used
for each samples. Hence, it is natural that one would expect distribution
of expression values would more or less similar. Therefore, it is necessary
to remove variation between arrays and the methods to remove variation
among arrays, and is called array normalization methods. There are several
methods to make empirical distribution of expression values over all arrays.
Some of the methods are:
(i) Normalization by mean
(ii) Median or Q2 normalization
(iii) Q3 normalization
(iv) Quantile normalization
In normalization by mean method, the expression values are transformed
so that that mean of all the arrays is same. Median method transforms
the expression values so that all arrays have median same as that of some
reference array. The Q3 method is defined as similar to Q2. In Q3, third
quartiles of the arrays are calculated to the third quartile of the mock array.
Quantile normalization method is an extension of Q2 and Q3 normalization.
This method is based on transforming each array specific distributions of
intensities so that they all have similar values of quantiles.
2.3.2

Normalization for two channel experiment

In a two channel microarray experiment, two different samples are labeled
by two fluorescent dyes R and G, hybridized on an array. The difference of
intensities between two channels gives DEGs, provided that these variations
are only because of biological functioning of the genes in different conditions.
Here, to identify DEGs it is necessary to compare the intensity of R and G.
These methods are applied on the Log of the ratio R/G.

3

Applications of Microarrays

Microarrays have been utilized in several biomedical problems including
gene discovery, disease diagnosis, pharmacogenomics, and toxicology. For
instance, microarrays can be used to identify disease genes by comparing
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expression patterns of genes in disease versus normal cells sample. Similarly, it can also be used to identify possible abnormal gene expression and
abnormal interaction between genes for a disease.
For a majority of applications, microarrays address four broad categories
of problems (Xu, 2008):
(i) Gene selection/gene filtering or identification of differentially expressed
genes (DEGs)
(ii) Finding natural groupings among genes, conditions or both (clustering)
(iii) Patient classification using gene expression
(iv) Finding regulatory relationships among given set of genes

3.1

Gene Selection or Identification of DEGs

An important purpose for monitoring expression level of genes is to identify
those genes which are differentially expressed across two kinds of tissue samples or samples observed under two different experimental conditions. Set
of genes differentially expressed over two different samples, i.e., normal and
cancerous tissue, are expected to give clues about cancer mechanism. A large
variety of methods exists for finding differentially expressed genes and most
of these methods are based on statistical techniques, such as fold-change
(Schena et al., 1995), t-test statistics (Peck and Devore, 2011; Drăghici,
2003), ANOVA (Kerr et al., 2000), rank product (Breitling et al., 2004),
Significant Analysis of Microarray (Tusher et al., 2001), Random Variance
Model (Wright and Simon, 2003), Limma (Smyth, 2004), and so on. Review
on the various methods can be found in: (Pan, 2002; Jeffery et al., 2006).
Fold change is one of the simplest ad-hoc methods often used in microarray analysis. A fold change is a measure that describes how much expression
level of a gene changes over two different samples (conditions) or groups. To
calculate a fold change, the average of expression values for each probes are
calculated across the samples in each group, and then ratios of these average
are taken. The levels of fold change are observed and genes under or above
a thresholds are selected. For example, fold change below 0.5 is considered
as down-regulated and fold change above 2.0 is considered as up-regulated.
The Rank Products method is based on the statement that an experiment
examining for n genes in m replicas, will have probability to be ranked first
of 1/nm, if the list values were totally random. Hence, it is improbable that
single gene to have top position in all the given replicas, if given gene was
not expressed differentially. Then, genes can be sorted based on likelihood
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of observing their rank product values (Jeffery et al., 2006). The two-sample
t-test is widely used parametric hypothesis testing method for the identification of DEGs. The t-statistics gives a probability value (p-value) for each
gene. A small p-value indicates that genes are differentially expressed under
the hypothesis that there is no differential expression, which is not true.
The t-statistic is calculated as the difference in the means over the standard
deviation. Raza and Mishra (2012) proposed an anticlustering gene algorithm for the identification of genes as drug target, where they applied a
combination of statistical techniques.

3.2

Clustering Genes, Samples or Both

Clustering is a means of analysing set of objects by grouping them into
different clusters based on some similarity measures. Basically clustering
is an unsupervised technique that groups the similar objects into clusters.
Researchers can apply clustering techniques to cluster gene expression data.
Hence, genes belonging to a particular cluster are supposed to share common
properties. If gene expression profiles (genes) are clustered, one may discover
set of genes co-regulated in a certain samples. Similarly, gene expression can
be grouped by clustering its samples. Sample clustering is done when it is
needed to identify subgroups of certain condition (for instance, disease).
The third means of grouping the gene expression data is to cluster both
rows (genes) as well as columns (samples), which are known as co-clustering
or bi-clustering. This kind of clustering helps us to find groups of genes
associated with group of samples (patient). Some of the most popularly
used clustering techniques are k-means, hierarchical, SOM, fuzzy c-means,
non-Euclidean relational fuzzy c-means.
Using Microarray gene expression data, distance between two expressed
genes can be computed so that it can be known that whether genes are
interrelated or not, and placed in same cluster. Euclidean distance, Manhattan distance and Pearson correlation distance are some of the commonly
applied distance measures. In Raza (2014), four different clustering techniques viz., k-means, Hierarchical clustering, density based clustering and
Euclidean method based clustering, have been applied on five different types
of cancer gene expression data (lung cancer, prostate cancer, colon cancer,
breast cancer and ovarian cancer). In all these five datasets, there are large
numbers of genes compared to numbers of samples. Hence, for better learning on machine learning techniques and to avoid the curse of dimensionality
problem, after data normalization, attribute reduction using t-test has been
done at a significance level of 0.001. There is no single clustering algorithm
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that can work well in all the situations. Selection of a particular clustering
approach depends on the problem at hand and the dataset under study.

3.3

Patient Classification

Gene expression data can be used to train a classifier so that it can recognize a given condition (e.g. class label such as normal or cancerous). The
advantage of this kind of classification is that once a classifier is trained with
gene expression profiles to recognize a patient class, then it can recognized a
class of unknown patient for which the classifier has not been trained. There
are several supervised techniques available for patient classification, such as
Bayesian networks, M5 model tree, k-nearest neighbourhood, Random forest, neural networks, and support vector machines.
In Raza and Hasan (2013), authors have done a comparatively evaluation
of various machine learning techniques for their accuracy in class prediction
of prostate cancer based on Microarray dataset. As per their evaluation,
Bayes Net gave the best accuracy for prostate cancer class prediction with an
accuracy of 94.11%. Bayes Net is followed by Navie Bayes with an accuracy
of 91.17%. The objective of evaluating various machine learning techniques
is to come up with the best technique in terms of prediction accuracy and to
reveal a good procedure for meaningful attribute reduction. A similar kind
of process may be used to classify other types of cancers. One of the biggest
challenges is to develop a single universal classifier which would be capable of
classifying all types of cancer gene expression data into meaningful number
of classes.

3.4

Finding Regulatory Relationship among Genes

A gene regulatory network (GRN) is a network of interaction among genes,
where node represents genes and interconnection between them represents
their regulatory relationship. Today, one of the most exciting problems in
systems biology research is to decipher how the genome controls the development of complex biological system. Microarrays have been widely used
to find out new (unknown) regulatory mechanism. The discovery of GRN
using gene expression data is known as reverse-engineering of GRN. The
GRNs help in identifying the interactions between genes and provide fruitful information about the functional role of individual genes in a cellular
system. They also help in diagnosing various diseases including cancer.
In the last several decades, many computational methods have been
proposed to discover complex regulatory interactions among genes based

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on microarray data. These techniques can be clubbed into different groups,
such as Boolean networks (Liang et al., 1998; Akutsu et al., 1999; Shmulevich
et al., 2002; Martin et al., 2007; Raza and Jaiswal, 2013; Raza and Parveen,
2013), Bayesian networks (Friedman et al., 2000; Husmeier, 2003), Petri
nets (Koch et al., 2005; Remy et al., 2006), linear and non-linear ordinary
differential equations (ODEs) (Chen et al., 1999; Tyson et al., 2002; De Jong
and Page, 2008), machine learning approaches (Weaver et al., 1999; Kim
et al., 2000; Vohradský, 2001; Keedwell et al., 2002; Huang et al., 2003;
Tian and Burrage, 2003; Zhou et al., 2004; Xu et al., 2004; Hu et al., 2006;
Jung and Cho, 2007; Xu et al., 2007a,b; Chiang and Chao, 2007; Lee and
Yang, 2008; Datta et al., 2009; Zhang et al., 2009; Maraziotis et al., 2010;
Ghazikhani et al., 2011; Liu et al., 2011; Kentzoglanakis and Poole, 2012;
Noman et al., 2013), etc.
For review of the modeling techniques and the subject, refer to (De Jong,
2002; Wei et al., 2004; Schlitt and Brazma, 2007; Cho et al., 2007; Karlebach
and Shamir, 2008; Swain et al., 2010; Sı̂rbu et al., 2010; Mitra et al., 2011;
Raza and Parveen, 2013).

4

Artificial Intelligence Techniques and Microarray Analysis

Artificial Intelligence (AI) is an interdisciplinary field of study where the
goal is to create intelligence by a machine or a computer program. Most
of the researchers defines AI as the study and design of intelligent agents,
where an intelligent agent is a system that can perceive given environment to
take actions and maximize its probability of being success. John McCarthy
coined this term in 1955 and defined AI as the science and engineering of
making intelligent machine. AI has a vast domain of research including reasoning, knowledge, learning, natural language processing, perception, etc.
Most popularly used AI techniques for solving real-life problems, including bioinformatics problems such as analysis of microarray array data to
extract fruitful knowledge, are statistical methods and computational intelligence. Among the computational intelligence, artificial neural networks,
fuzzy systems, evolutionary computations and many statistical tools are
mostly applied AI based approach.
In this section, few computational intelligence approaches and their applications in Microarray analysis have been briefly described.

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4.1

Artificial Neural Networks

Artificial Neural Networks (ANNs) are massively parallel computing system
which is inspired by biological system of neurons. It is collection of extremely large number of simple processing elements, called neurons, having
many interconnections. These elements have inputs, which are multiplied
by weights and then computed by a mathematical function, called activation function, regulating the activation of the neuron. A weight value wij
is assigned to each connection and hence the net input to the neuron is the
weighted sum of its n input signals xi , i = 1, 2, .., n. Each neuron has an activation function f (generally a sigmoidal function), which is used to compute
the neurons current activation ai, and output function g (generally identity
function) which is used to compute value Oi . By adjusting the weights of
neurons, the desired output can be obtained from the inputs. The process
of adjusting the synaptic weights is known as learning or training process.
The most widely applied training algorithm is Backpropagation algorithm.
The first neural network model was proposed by McCulloch and Pitts
in 1993 and since then hundreds of different models have been developed
(Gershenson, 2003). The differences in the neural networks might be in
their architecture, activation function, and topology, training algorithm and
accepted input and output values. On the basis of architecture (connection
pattern), neural networks can be broadly clubbed into two groups (Jain
et al., 1996): i) feedforward networks, where there is no loop and ii) recurrent networks, which contain loops because of feedback connections. A
typical network from each category is shown in Fig. 4. Feedforward networks
are memory-less, i.e., their output is independent of the previous network
state but recurrent networks consider network feedback paths, which are
with memory, i.e., the current output is dependent on the previous state
of the network. Different network architecture needs appropriate training
algorithm. The learning paradigms can be categorized as: supervised, unsupervised and hybrid (reinforcement).
Vohradsky (Vohradský, 2001) applied ANN to model gene regulation
by assuming that the regulatory effect on gene expression of a particular
gene can be expressed in the form of ANN. Each neuron in the neural network represents a gene and connectivity between them represents regulatory
interactions. Here each layer of the ANN represents the level of gene expression at time t and output of a neuron at time t + ∆t can be determined
from the expression at time t. The advantage the model is that it is continuous, uses a transfer function to transform the inputs to a shape close
to those observed in natural processes and does not use artificial elements.
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Figure 4: A taxonomy of feed-forward and recurrent network architectures
Keedwell and his collaborators (Keedwell et al., 2002) also applied ANN
in the purest form for the reconstruction of GRNs from microarray data.
The architecture of the neural network was quite simple when dealing with
Boolean networks. Hu et al. (2006) has proposed a general recurrent neural network (RNN) model for the reverse-engineering of GRNs and to learn
their parameters. RNN has been deployed due to its capability to deal with
complex temporal behaviour of genetic networks. In this model, time delay
between the output of a gene and its effect on another gene has been incorporated. A more recent work by Noman and his colleagues (Noman et al.,
2013) proposed a decoupled-RNN model of GRN. Here, decoupled means dividing the estimation problem of parameters for the complete network into
several sub-problems, each of which estimate parameters associated with
single gene. This decoupled approach decreases the dimensionality problem
and makes the reconstruction of large network feasible. In one of our work
(Raza et al., 2014), we also proposed a RNN based hybrid model of GRN
that uses extended Kalman filter to estimate and update synaptic weights
using Backpropagation Through Time (BPTT) training algorithm.
The ANN approach of GRN inference works well for small size network,
i.e., a network of up to 100 genes. This is because of less number of available
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samples in a Microarray experiment. As the size of the network grows, the
number of unknown parameters (interactions) also grows, and that requires
a very large number of Microarray samples, which is rarely available in
Microarray data.

4.2

Fuzzy System

Fuzzy logic is based on the concept of partial truth, i.e., truth values between completely true and completely false. For example, using fuzzy logic,
propositions can be denoted with degrees of truthfulness and falsehood with
the help of a membership function. L.A. Zadeh (Zadeh, 1996) was the first
who introduced the concept of fuzzy logic to represent vagueness in linguistics and implement and express human knowledge and inference capability
in a natural way. In broad sense, fuzzy logic is an extension of multivalue
logic. In specific sense, fuzzy logic is a logic system which can be used to
model approximate reasoning (Cao, 2006). Fuzzy logic has been proved to
be useful in expert system and other artificial intelligence applications.
A fuzzy system generally consists of three parts:
(i) fuzzy input and output variables, and their fuzzy values,
(ii) fuzzy rules, such as Zadeh-Mamdani’s fuzzy rules, Takagi-Sugeno’s
fuzzy rules, gradual fuzzy rules and recurrent fuzzy rules,
(iii) fuzzy inference methods, which may include fuzzification and defuzzification.
The biological systems behave in a fuzzy manner. Fuzzy logic provides
a mathematical framework for modeling and describing biological systems.
Literature reports that fuzzy logic has been successfully used for the analysis of microarray data due to its capability to represent non-linear systems, its friendly language to incorporate and edit domain knowledge in
the form of fuzzy rules (Raza and Parveen, 2013). Woolf and Wang (Woolf
and Wang, 2000) proposed a fuzzy logic based algorithm for analysing gene
expression data. The proposed fuzzy model was designed to extract gene
triplets (activators, repressors, targets) in yeast gene expression data. The
model took 200 hours to analyse the relationships between 1,898 genes on
an 8-processor SGI Origin 2000 system. Later, Ressom and his colleagues
(Ressom et al., 2003) extended and improved the work of Woolf and Wang
(Woolf and Wang, 2000) in terms of reducing computation time and generalizing the model to accommodate co-activator and co-repressors, in addition
to activators, repressors and targets. Reduction in computation time is
achieved by applying clustering as a pre-processing step. The improved algorithm achieves a reduction of 50% computation time. After 3 years, Ram
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and his colleagues (Ram et al., 2006) also improved the Woolf and Wangs
fuzzy logic model to predict changes in gene expression values and extracted
causal relationship between genes. They have improved searching for activator/repressor regulatory relationship between gene triplets in the microarray
data. A pre-processing technique for the fuzzy model has also been proposed
to remove redundant data present that makes the model faster. Sun and colleagues (Sun et al., 2010) applied dynamic fuzzy approach by incorporating
structural knowledge to model gene regulatory networks using microarray
gene expression data. This technique infers gene interactions in the form
of fuzzy rules and able to reveal biological relationships among genes and
their products. The distinguishing feature of this model is that (i) prior
structural knowledge on GRN can be incorporated for the purpose of faster
convergence of the identification process and (ii) non-linear dynamic property of the GRN can be well captured for the better prediction.
As discussed in previous section, clustering (grouping) Microarray data
is also one aspect of analysing it that gives clues about set of co-regulated
genes. Fuzzy based clustering algorithm, called fuzzy c-means (FCM) was
first introduced by Dunn in 1973 (Dunn, 1973) but implemented by Bezdek
(Bezdek, 1981). The FCM has now become most popular fuzzy clustering
algorithm and considered as robust to scale the dataset (Wang et al., 2008).
A major problem with FCM algorithm for clustering microarray data is the
selection of the fuzziness parameter m. The work of Dembélé and Kastner
(2003) shows that the commonly used value m = 2 is not always appropriate.
The optimal value for m varies from one dataset to another. They also
proposed an empirical method to estimate an adequate value for m, based
on the distribution of distances between genes in the given dataset.
In addition to fuzzy-clustering hybrid, fuzzy logic has been hybridized
other computational intelligence techniques, including fuzzy and neural network hybrid (called neuro-fuzzy) and fuzzy and genetic algorithm (called
fuzzy-genetic). Neuro-fuzzy and fuzzy-genetic hybrid have been successfully
applied for GRN inference using Microarray data. The review of the application of neuro-fuzzy and neuro-genetic for GRN inference can be found in
(Mitra et al., 2011; Raza and Parveen, 2013).

4.3

Evolutionary Computation

Evolutionary computing is a collection of problem-solving techniques based
on principles of biological evolution. The functional analogy of evolutionary
computing is the natural evolution that relates to a particular kind of problem solving grounded on trial-and-error process. Natural selection means
17

that we have a population of individuals that strive for survival and reproduction. The fitness of these individuals determines how well they succeed
in achieving their goals, i.e., presenting their chance for survival and reproduction. Charles Darwin formulated the theory of natural evolution. Over
several generations, biological organism evolves according to the principle
of natural selection like survival of the fittest. The history of evolutionary
computing goes back to 1940s. After many decades of research in this area,
researchers came up with many evolutionary computing techniques such as
evolutionary programming, evolution strategies, genetic algorithm (Holland,
1975) and generic programming (Koza, 1992).
Genetic algorithms (GAs) are basically optimization techniques inspired
by Darwins theory of evolution. In fact, it is a search algorithm based on
the mechanism of natural selection and survival for the fittest. Here, searching in a population is done from a single point and competitive selection is
done in each iterations. The solutions having high fitness are recombined
with other solutions and then mutated by changing the single element of
the solution. The purpose of genetic operators, such as crossover and mutation, are to generate new population of solutions for the next generation.
Genetic algorithms belong to probabilistic algorithms and are different from
random search algorithms because former combines elements of directed and
stochastic search. Due to this reason, GAs are found to be more robust than
directed search methods. Further, GAs maintain a population of potential
solutions; on the other hand, other search techniques process a single point
of search space (Raza and Parveen, 2012).
Noman and Iba (2005) applied decoupled S-system approach for the inference of effective kinetic parameters from time series gene expression data
and applied Trigonometric Differential Evolution (TDE) for the optimization and captures the dynamics of gene expression. Later, Chowdhury and
Chetty (2011) extended the work of Noman and Iba (2005) and applied GA
for scoring the networks several useful features, such as a Prediction Initialization (PI) algorithm to initialize the individuals, a Flip Operation (FO)
for matching values. A refinement algorithm for optimizing sensitivity and
specificity of inferred networks was also proposed. Xu and colleagues (Xu
et al., 2009) proposed genetic programming based method for the analysis of
microarray datasets, where genetic programming performs classification and
feature selection simultaneously. Maulik (2011) studied the performance of
three most commonly used computational techniques such as genetic algorithm, simulated annealing and differential evolution for developing fuzzy
clusters of gene expression data. Clustering is an unsupervised analysis
approach for grouping co-expressed genes together. To improve results of
18

clustering, support vector machine (SVM) has been utilized. A review of application of evolutionary computation for Microarray analysis can be found
in Sı̂rbu et al. (2010); Mitra et al. (2011); Raza and Parveen (2012).

4.4

Other AI Based Methods

Machine learning algorithms, like ANN, are also used to predict interactions
between genes of a GRN using Microarray data. But, these algorithms
are so complex and work like a black-box. Black-box model means what
is happening inside the algorithm is hidden (Sı̂rbu et al., 2010). On the
other hand, nature-inspired algorithms, in comparison to other algorithms,
are simpler in nature and they have been found to be applied in various
biological problems from simplest like alignment of sequences to the complex
like protein structure prediction (Pal et al., 2006). One such type of natureinspired algorithm is Genetic algorithm which has already been discussed in
the previous section.
In the last two decades, several nature-inspired metaheuristic optimization algorithms have been proposed and successfully applied in many optimization problems, including microarray analysis. Fister and colleagues
(Fister Jr et al., 2013) have done a survey of nature-inspired optimization
algorithm and listed 7̃5 nature-inspired algorithms proposed by different
researchers, and classified these algorithms into four groups: Swarm intelligence based , Bio-inspired based, Physics-based and Chemistry-based, and
Others. algorithms. Ant colony optimization (ACO) is one of the natureinspired swarm-based optimization algorithm proposed by Marco Dorigo in
1992 (Dorigo, 1992) in his PhD thesis. ACO is a metaheuristic optimization technique where a set of artificial ants search for optimal solutions in
a given optimization problem. Ants use pheromones laid by the other ants
as footmarks to follow. Hence, ant reaches the food source by the shortest
path using knowledge gained by the other ants. This algorithm can be used
for optimization problems, including gene interaction network optimization.
ACO has been applied to several bioinformatics problems including sequence
alignment, drug designing, 2D protein folding and biological network optimization. Raza and Kohli (2015) applied ACO algorithm for inferring highly
correlated key gene interactions in a GRN that plays an important role in
identifying biomarkers for disease which further helps in drug design. The
limitation of proposed algorithm by Raza and Kohli (2015) is that it can
find out a total number of interactions equal to total number of genes.
PSO has been applied for clustering and feature (genes) selection in microarray data. A k-means clustering based upon PSO has been proposed
19

for microarray data clustering by Deng et al. (2005). The algorithm discovers clusters in microarray data without having any prior knowledge of
feasible number of clusters. Chuang et al. (2009) applied Binary PSO for
feature selection in microarray data. Sahu and Mishra (2012) also proposed
a PSO based feature selection algorithm for cancer microarray data. For
the selection of efficient genes from thousands of genes, Chen et al. (2014)
proposed an approach utilizing PSO combined with a decision tree classifier. For the biclustering of microarray data, a comparative study on three
nature-inspired algorithms, such as PSO, Shuffled Frog Leaping (SFL) and
Cuckoo Search (CS) algorithms, have been done on benchmark gene expression dataset by Balamurugan et al. (2014). The result reports that CS
outperforms PSO and SFL for 3 out of 4 datasets. The classification accuracy of simple statistical learning techniques can be enhanced when natureinspired algorithm are applied for the feature selection. One such study has
been carried out by Gunavathi and Premalatha (2014). They performed a
comparative analysis of swarm intelligence techniques, such as PSO, cuckoo
search (CS), SFL, and SFL with Lévy flight (SFLLF), for feature selection in
cancer classification. The k-nearest neighbour (kNN) classifier is applied to
classify the samples. The result shows that k-NN classifier through SFLLF
feature selection method outperform PSO, CS, and SFL. Sometimes, DEGs
techniques are used for gene selection/filtering or dimension reduction in
microarray data where we have a large number of genes (features). The
dimension reduction is a preprocessing step whenever we use a machine
learning technique for training with gene expression datasets where number
of gene are larger than the available samples (generally known as curse of
dimensionality problem).
Due to advancement in data mining algorithms and tools, it is a keen interest of the researchers to apply these tools to identify patterns of interest in
the gene expression data. Association rule mining is one of the most widely
used data mining technique that have been applied for gene expression mining by a number of researchers Creighton and Hanash (2003). Association
rules mining may discover biologically useful associations between genes,
or between different biological conditions using microarray gene expression
data. An association rules are written in the form A1 → A2 , where A1 and
A2 are disjoint sets of data items. The set A2 is likely to occur whenever
the set A1 occurs. Here, the data items may present either highly expressed
or repressed genes, or any other facts that state the cellular environment
of genes (e.g. diagnosis of a disease samples) (Raza, 2015). Formal Concept Analysis (FCA), introduced by R. Wille in early 1980s, is another data
mining technique based on lattice theory. It has been widely used for the
20

analysis of binary relational data. Like other computational techniques,
FCA has also been applied in microarray analysis, gene expression mining,
gene expression clustering, finding genes in gene regulatory networks, and
so on. A review FAC for the analysis and knowledge discovery from gene
expression and other biological data can be found in Raza (2015).

5

Conclusions, Discussions and Future Challenges

The bioinformatics is an interdisciplinary area of study where one of the
objectives is to deal with the analysis and interpretation of large sets of
data generated from various large-scale biological experiments, including
Microarrays. Microarray technology is one of the powerful tools used to
measure genome wide expression levels of genes. As microarrays technologies have become more prevalent, the challenges associated with collecting,
managing, and analyzing the data from each experiment have essentially
increased. With the help of these technologies, researchers can find out
answer of some challenging questions like: (i) what are the functions of different genes? (ii) In what cellular processes do they participate? (iii) how
genes are regulated? (iv) how genes and its products (proteins) do interact,
and what are these interaction networks? (v) how expression level of genes
differs in different cell types and states? (vi) how expressions of genes are
affected by various disease or drug treatments?
In this chapter, four broad categories of problems have been tackled for
the analysis of Microarrays:
(i) Identification of differentially expressed genes: It helps us in the selection of few relevant genes and elimination of irrelevant genes for
further study. It also solves the dimensionality problem of machine
learning techniques by filtering differentially expressed genes over various samples and training a classifier with the selected number of genes
only.
(ii) Cancer classification using gene expression: Classification of patient
based on gene expression profile is another important issue for the
analysis of microarray data. The application of AI-based techniques
for cancer classification based microarray data has been discussed.
(iii) Clustering genes, conditions or both: Another important aspect of analyzing microarray data is finding natural groupings among genes, which
can be done using clustering techniques. Clustering is an unsupervised

21

learning technique that plays a vital role in providing a class label to
unlabeled data and it can be used to identify set of co-regulated genes.
(iv) Inferring gene interaction network: The gene interaction network plays
an important role in identifying root-cause of various diseases. Inferring gene interaction network from gene expression profiles is one of
other aspect of analyzing microarray data. The application of AI-based
techniques for GRN inference has been covered in length and various
resources for further study has been listed.

Future Challenges
Microarray technology is a high-throughput experimental approach that
measures the genome-wide expression of genes and data are produced in
large-scale. Hence, analysing these data to infer useful information is big
challenge. Some of the future directions for the analysis of microarray data
are as follows:
(i) One of the main drawbacks of microarray technology is that data generated by these experiments contain noises and are not much reliable.
Hence, before the data is analysed, we must apply sophisticated noise
removal and data normalization technique.
(ii) Application of machine learning techniques in genome-wide analysis
of microarray data creates the problem of dimensionality. Hence,
some techniques are required to identify differentially expressed genes
(DEGs). Statistical techniques, such as fold change, t-test, ANOVA,
etc. dominates in the identification of DEGs. Hence, it is needed
to explore the application of computational intelligence to tackle the
problem of DEGs.
(iii) Another biggest challenge is to develop a single classifier which is best
suitable for classification of all types of cancer gene expression data into
meaningful number of classes. Nature inspired optimization techniques
such as Ant Colony Optimization (ACO) (Dorigo, 1992), Artificial Bee
Colony optimization (ABC) Karaboga (2005), Cuckoo Search Yang
and Deb (2009), Particle Swarm Optimization (PSO) Kennedy and
Eberhart (1995), Spider Monkey Algorithm (Bansal et al., 2014) and
so on, are successfully being used in many challenging problems. In
the future work, one can hybridize these nature inspired optimization
techniques with different classifiers for better classification accuracy.
22

(iv) For the gene clustering problem, one can apply fuzzy based clustering
techniques (such as Fuzzy C-Means) to group genes or patient or both.
Even, ranked based classification techniques can be applied.
(v) Inference of gene interaction networks using gene expression profile is
another open area where computational intelligence can be applied to
identify interactions among given set of genes. Hybrid algorithms (for
example, fusion of neural networks, genetic algorithms and/or fuzzy
logic and other nature-inspired algorithms) can be applied for the said
purpose.

Acknowledgements
The author acknowledges the funding received from University Grants Commission, Govt. of India through research grant 42-1019/2013(SR).

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