Dimension reduction methods for microarray data: a review

Abstract

Dimension reduction has become inevitable for pre-processing of high dimensional data. “Gene expression microarray data” is an instance of such high dimensional data. Gene expression microarray data displays the maximum number of genes (features) simultaneously at a molecular level with a very small number of samples. The copious numbers of genes are usually provided to a learning algorithm for producing a complete characterization of the classification task. However, most of the times the majority of the genes are irrelevant or redundant to the learning task. It will deteriorate the learning accuracy and training speed as well as lead to the problem of overfitting. Thus, dimension reduction of microarray data is a crucial preprocessing step for prediction and classification of disease. Various feature selection and feature extraction techniques have been proposed in the literature to identify the genes, that have direct impact on the various machine learning algorithms for classification and eliminate the remaining ones. This paper describes the taxonomy of dimension reduction methods with their characteristics, evaluation criteria, advantages and disadvantages. It also presents a review of numerous dimension reduction approaches for microarray data, mainly those methods that have been proposed over the past few years.

Full text

AIMS Bioengineering, 4(2): 179-197.
DOI: 10.3934/bioeng.2017.2.179
Received: 27 November 2016
Accepted: 01 March 2017
Published: 07 March 2017
http://www.aimspress.com/journal/Bioengineering
Review
Dimension reduction methods for microarray data: a review
Rabia Aziz *, C.K. Verma, and Namita Srivastava
Department of Mathematics & Computer Application, Maulana Azad National Institute of
Technology Bhopal-462003 (M.P.) India
* Correspondence: Email: rabia.aziz2010@gmail.com.
Abstract: Dimension reduction has become inevitable for pre-processing of high dimensional
data. “Gene expression microarray data” is an instance of such high dimensional data. Gene
expression microarray data displays the maximum number of genes (features) simultaneously at a
molecular level with a very small number of samples. The copious numbers of genes are usually
provided to a learning algorithm for producing a complete characterization of the classification task.
However, most of the times the majority of the genes are irrelevant or redundant to the learning task.
It will deteriorate the learning accuracy and training speed as well as lead to the problem of
overfitting. Thus, dimension reduction of microarray data is a crucial preprocessing step for
prediction and classification of disease. Various feature selection and feature extraction techniques
have been proposed in the literature to identify the genes, that have direct impact on the various
machine learning algorithms for classification and eliminate the remaining ones. This paper
describes the taxonomy of dimension reduction methods with their characteristics, evaluation criteria,
advantages and disadvantages. It also presents a review of numerous dimension reduction
approaches for microarray data, mainly those methods that have been proposed over the past few
years.
Keywords: DNA microarrays; dimension reduction; classification; prediction
1. Introduction
The theory of microarrays methodology was first introduced and demonstrated by Chang TW in
1983, for antibody microarrays in a scientific publication and registered a series of patents [1]. In
1990s, Microarrays were developed as a consequence of the efforts to speed up the process of drug
discovery [2]. Traditional drug discovery was shaped for developing a number of candidate drugs

180
AIMS Bioengineering Volume 4, Issue 2, 179-197.
and trying them one by one against diseases of interest. The long and limited method of trial and
error based centered drug discovery could not be very effective for some perticular diseases. A group
of researchers at affymax established a photolithography system to achieve this in a fashion similar
to the synthesis of VLSI (Very Large Scale Integration) chips in the semiconductor industry [3]. The
first version developed by the sister company Affymetrix came to be known as the Gene chip [4,5,6].
The “gene chip” industry started to grow significantly after the 1995 with the publication of Science
Paper by the Ron Davis and Pat Brown labs at Stanford University. Simultaneously researchers at the
Pat Brown’s lab of Stanford University developed a different type of microarray [7]. With the
establishment of companies such as Affymetrix, Agilent, Applied Microarrays, Arrayit, Illumina, and
others. The technology of DNA microarrays has become the most sophisticated and the most widely
used, while the use of protein, peptide and carbohydrate microarrays is expanding [8].
In the past few years, multivariate statistics for microarray data analysis has been the subject of
thousands of research publications in Statistics, Bioinformatics, Machine learning, and
Computational biology. Most of the traditional issues of multivariate statistics have been studied in
the context of high-dimensional microarray data. The main types of data analysis needed for
biomedical applications include [9,10]:
 Gene Selection: the procedure of feature selection, that finds the genes, strongly associated with a
particular class.
 Classification: classifying diseases or predicting outcomes based on gene expression patterns, and
perhaps even identifying the best treatment for given genetic signature [10].
 Clustering: finding new biological classes or refining existing ones [11].
Clustering can be used to find groups of similarly expressed genes in the aspiration of finding
that both have a similar function [12]. On the other hand, another topic of interest is the classification
of the microarray data for prediction of disease such as cancer using gene expression levels [13].
Classification of gene expression data samples involves dimension reduction and classifier design.
Thus, in order to analyze gene expression profiles correctly, dimension reduction is an important
process for the classification [14]. The goal of microarray data classification of cancer is to build an
efficient and effective model that can differentiate the gene expressions of samples, i.e. classify
tissue samples into different classes of the tumor. Nearest neighbor classification, Artificial Neural
Network, Bayesian, Decision tree, Random forest methods and Support Vector Machine (SVM), are
the most well-known approaches for classification. An overview of the methods mentioned above
can be found in Lee et.al. [15].
Recently, many gene expression data classification and dimension reduction techniques have
been introduced. You W et al. applied feature selection and feature extraction for dimension
reduction of microarray by using Partial Least Squares (PLS) based information [16]. Xi M et al.
used a binary quantum-behaved Particle Swarm Optimization and Support Vector Machine for
feature selection and classification [17]. Wang et al. proposed a new tumor classification approach
based on an ensemble of Probabilistic Neural Networks (PNN) and neighborhood rough set models
based on gene selection [18]. Shen et al. proposed a modified particle swarm optimization that
allows for the simultaneous selection of genes and samples [19]. Xie et al. developed a diagnosis
model based on IFSFFS (Improved F-score and Sequential Forward Floating Search) with support
vector machines (SVM) for diagnosis of erythema to squamous diseases [20]. Li et al. proposed an
algorithm with a locally linear discriminant embedded in it, to map the microarray data to a low

181
AIMS Bioengineering Volume 4, Issue 2, 179-197.
dimensional space, while Huang et al. recommended an upgraded decision forest method for the
classification of microarray data that used a built-in feature selection method for fine-tuning [21,22].
In subsequent years, the use of gene expression profiles for cancer diagnoses has been the major
focus in many microarray studies. Various gene selection methods and classification algorithms are
proposed in the literature which are able to reduce the dimensionality by removing irrelevant,
redundant and noisy genes for accurate classification of cancer [23].
2. Microarray Gene Expression Data Analysis Challenges
Microarray technology provided biologists with the ability to measure the expression levels of
thousands of genes in a single experiment. The data from microarray consists of a small sample size
and high dimensional data. A characteristic of gene expression microarray data is that the number of
variables (genes) m far exceeds the number of samples n, commonly known as “curse of
dimensionality” problem. Processing of microarray gene expression data is shown in Figure 1. To
avoid the problem of the “curse of dimensionality”, dimension reduction plays a crucial role in DNA
microarray analysis. Microarray experiments provide huge amount of data to the scientific
community, without appropriate methodologies and tools, significant information and knowledge
hidden in these data may not be discovered. The vast amount of raw gene expression data leads to
statistical and analytical challenges [24]. The challenge experienced by statisticians is the nature of
the microarray data. The best statistical model will largely depend on the total number of possible
gene combinations. Therefore, the impact of microarray technology on biology will depend heavily
on data mining and statistical analysis. Conventional statistical methods give improper result due to
high dimension of microarray data with limited number of patterns. Therefore, there is a need for
methods capable of handling and exploring large data sets. The field of data mining and machine
learning provides a wealth of methodologies and tools for analyzing large data sets. A sophisticated
data-mining and analytical tool is required to correlate all of the data obtained from the arrays and
which can help to group them in a meaningful way [25].
Figure 1. Formation of microarray gene expression data.

182
AIMS Bioengineering Volume 4, Issue 2, 179-197.
Data mining with machine learning is a process to discover meaningful, non-trivial, and
potentially useful knowledge (patterns, relationships, trends, rules, anomalies, dependencies) from
the large amount of data by using different types of automatic or semi-automatic techniques.
Compared with classical statistical approaches, data mining is feasibly best seen as a process that
incorporates a wider range of integrated methodologies and tools, including databases, machine
learning, knowledge-based techniques, network technology, modeling, algorithms, and uncertainty
handling [26].
Gene expression data of DNA microarray which represent the state of a cell at a molecular level,
have a great prospective as a medical diagnosis tool [27]. Typical microarray data mining analysis
include discriminant analysis, regression, clustering, association and deviation detection. Several
machine learning techniques such as, Support Vector Machines (SVM) [28], k-Nearest
Neighbours (kNN) [29], Artificial Neural Networks (ANN) [30], Naïve Bayes (NB) [31], Genetic
Algorithms, Bayesian Network, Decision Trees, Rough Sets, Emerging Patterns, Self-Organizing
Maps, have been used by different research for different analysis of microarray gene expression
data [32,33]. In classification, available training data sets are generally of a fairly small sample size
compared to a large number of genes involved. Theoretically, increasing the size of the genes is
expected to provide more discriminating power but in practice, large genes significantly slow down
the learning process. As well as cause the classifier to over fit the training data and compromise
model simplification. Dimension reduction can be used to successfully extract those genes that
directly influence the classification. In this paper, we focus our discussion on popular machine
learning techniques for dimension reduction and identification of potentially relevant genes for
molecular classification of cancer.
3. Different Dimension Reduction Techniques
For microarray data classification, the main difficulty with most of the machine learning
technique is to get trained with a large number of genes. A lot of candidate features (genes) are
usually provided to a learning algorithm, for constructing a complete characterization of the
classification task. In the past ten years, due to the applications of machine learning or pattern
recognition, the domain of features have expanded from tens to hundreds of variables or features
used in those applications. Several machine learning techniques are developed to address the
problem of reducing irrelevant and redundant features which are a burden for different challenging
tasks [34]. The next section is about feature selection methods (filters, wrappers, and embedded
techniques) applied on microarray cancer data. Then we will discuss feature extraction methods,
special case of feature selection method for microarray cancer data and the final section is about
combination of different feature selection method as a hybrid search approach to improve
classification accuracy and algorithmic complexity.
3.1. Feature selection
In machine learning, feature selection also known as variable selection, attribute selection or
variable subset selection. Feature selection is the process of selecting a subset of relevant and
redundant features from a dataset in order to improve the performance of the classification

183
AIMS Bioengineering Volume 4, Issue 2, 179-197.
algorithms in terms of accuracy and time to build the model [35]. The process of feature selection is
classified into three categories.
3.1.1. Filter
Filter methods use variable ranking methods as the standard criteria for variable selection by
ordering. Statistical ranking methods are used for their simplicity and good success is reported for
practical applications. A different suitable ranking criterion of statistics is used to score the variables
and select a threshold value in order to remove the variables below it. One definition that can be
mentioned, which will be useful for a feature is that “A feature can be regarded as irrelevant if it is
conditionally independent of the class labels” [36]. If a feature is to be relevant it can be independent
of the input data but cannot be independent of the class labels i.e. the feature that has no influence on
the class labels can be discarded [37]. The filter methods grouped in ranking and space search
methods according to the strategy utilized to select features [38]. Filter ranking methods select
features regardless of the classification model that are based on univariate and multivariate feature
ranking techniques. The process of feature selection that follow the filter methods is depicted in
Figure 2 [39]. This Figure shows that it selects features, which are similar to ones already picked.
This provides a good balance between independence and discrimination. Since the data distribution
is unknown, various statistical techniques can be used to evaluate different subsets of features with a
chosen classifier. Some of the popular technique found in literature that can be used for feature
ranking, with their advantage and disadvantage are listed in Table 1 [40].
Table 1. Advantages and disadvantages of filters methods.
Model search Advantages Disadvantages Examples
Filter
Univariate
Fast, Scalable
Independent of the classifier
Ignores feature dependencies
Some features which as a group have
strong discriminatory power but are weak
as individual features will be ignored
Features are considered independently
χ2
Euclidean distance
t-test
Information gain
Gain ratio
Multivariate
The models feature dependencies
Independent of the classifier
Better computational complexity
than wrapper methods
Slower than univariate techniques
Less scalable than univariate techniques
Ignores interaction with the classifier
Redundant features may be included
Correlation-based feature selection (CFS)
Markov blanket filter (MBF)
Fast correlation-based
feature selection (FCBF)
Different researchers used a different framework of filter methods in their works for the gene
selection of microarray data. Lin and Chien, used statistical clustering, based on linear relationship
and Coefficient correlation for Breast cancer cDNA micro-array data [41]. Sun et al. utilized local
learning based feature selection method for which key idea is to decompose an arbitrarily complex
nonlinear problem into a set of locally linear ones through local learning, and then learn feature
relevance globally within the large margin framework [42]. Zhu et al. used model-based entropy for
feature selection [43]. Some of the researchers used signal-to-noise ratio approach in a leukemia
dataset with k-fold and Holdout validation method [44]. Wei et al. developed two recursive feature

184
AIMS Bioengineering Volume 4, Issue 2, 179-197.
elimination methods, i.e. Feature score based recursive feature elimination (FS-RFE) and Subset
level score based re-cursive feature elimination (SL-RFE) [45]. Liu et al. proposed a novel method to
discover differentially expressed genes based on the Robust Principal Component
Analysis (RPCA) for the Colon dataset [46]. A prediction scheme was attempted by Maulik and
Chakraborty, that combines fuzzy preference based rough set (FPRS) method for feature (gene)
selection with semi supervised SVMs [47]. Chinnaswamy A and Srinivasan, advanced a hybrid
feature selection approach that combines the correlation coefficient with particle swarm
optimization [48]. Recently, a multiphase cooperative game theoretic feature selection approach has
been proposed for microarray data classification by Mortazavi et al. in 2016. The average
classification accuracy on eleven microarray data sets in this work shows that the proposed method
improves both average accuracy and average stability [49].
The benefits of variable ranking is computationally easy and avoids over fitting and is proven to
work well for certain datasets. Filter methods do not rely on learning algorithms which are biased
and is equivalent to changing data to fit the learning algorithm. One of the drawbacks of ranking
methods is that the selected subset might not be optimal because in that a redundant subset might be
obtained. Finding a suitable learning algorithm can also become hard since there is no underlying
learning algorithm for feature selection [50]. Also, there is no ideal method for choosing the
dimension of the feature space [40].
3.1.2. Wrapper methods
Unlike filter methods which use feature relevant criteria, the wrapper methods depend on the
performance of classifiers for obtaining a feature subset. Wrapper approach selects the feature subset
by using the induction algorithm as a black box (i.e. no knowledge of the algorithm is needed, just
the interface is required). The accuracy of the induced classifiers is estimated using accuracy
estimation techniques. The main problem of wrapper approach is state space search and different
search engines for different method [40]. The different number of search technique can be used to
find the best subset of features that maximizes the classification performance, e.g. Branch and Bound
method, Genetic Algorithm (GA), Particle Swarm Optimization (PSO). The goal of the search is to
find the state with the maximum evaluation, using a trial and error method to guide it. This heuristic
approach has a nice property that it forces the accurate estimation to execute cross-validation more
times on small datasets than on large datasets [51]. Sequential selection algorithms and Evolutionary
search algorithms are two main types of Wrapper methods. The advantage and disadvantage of both
the methods are shown in Table 2 with example.
a) Sequential selection algorithms
The sequential selection algorithm finds the minimum (or maximum) features by iterating the
process. The Sequential Feature Selection (SFS) algorithm starts with an empty set and adds one
feature for the first step that increases the performance of the objective function. From the second
step onwards the remaining features are added individually to the current subset and the performance
of new subset is calculated. By this process the best feature subset can be found that gives the
maximum accuracy of the classifier [40]. The process is repeated until the required numbers of
features are added. The Sequential Floating Forward Selection (SFFS) algorithm is more flexible

185
AIMS Bioengineering Volume 4, Issue 2, 179-197.
than the naive Sequential Floating Selection (SFS) because it introduces an additional backtracking
step.
Table 2. Advantages and disadvantages of wrapper methods.
Model search Advantages Disadvantages Examples
Wrapper
Sequential selection algorithms
Simple Interacts with the classifier
Small overfitting risk
Less computationally
Prone to local optima
Consider the dependence among features
Risk of over fitting
More prone than randomized
Algorithms for getting stuck in a
local optimum (greedy search)
Classifier dependent methods
The solution is not optimal
Sequential forward selection (SFS)
Sequential backward elimination (SBE)
Plus q take away r
Beam search
Evolutionary selection algorithms
Less prone to local optima
Interacts with the classifier
Models feature dependencies
Higher performance accuracy than filter
Computationally intensive
Discriminative power
Lower shorter training times
Classifier dependent selection
Higher risk of over-fitting
than deterministic algorithms
Simulated annealing
Randomized hill climbing
Genetic algorithms
Ant Colony Optimization
Rough set methods
Particle Swarm Optimization
Artificial Bee Colony (ABC)
Both the methods SFS and SFFS suffer from generating nested subsets since the forward
inclusion is always unconditional which means that two highly correlated features might be included
if it gave the highest accuracy in the SFS estimation. A modified Adaptive Sequential Forward
Floating Selection (ASFFS) method was developed to avoid the nesting effect [52]. ASFFS method
attempted to obtain a less redundant subset than the SFFS algorithm. Theoretically, the ASFFS
should produce a better subset of features than the SFFS but this is dependent on the objective
function and the properties of the data.
b) Evolutionary selection algorithms
An evolutionary selection algorithm is a method that might not always find the best solution but
definitely finds a good solution in reasonable time by sacrificing totality to increase efficiency. The
objective of an evolutionary is to produce a solution in a reasonable time frame that is good enough
for solving the problem at hand. The Evolutionary search algorithms evaluate different subsets to
optimize the performance of the objective function. Different feature subsets are produced either by
searching around in a search space or by generating solutions to the optimization problem.
Evolutionary algorithms are based on the ideas of biological evolution, such as reproduction,
mutation, and recombination, for searching the solution of an optimization problem. The main loop
of evolutionary algorithms includes the following steps:
1. Initialize and estimate the initial population.
2. Implement competitive selection.
3. Apply different evolutionary operators to generate new solutions.
4. Estimate solutions in the population.

186
AIMS Bioengineering Volume 4, Issue 2, 179-197.
5. Repeat from the second steps, until some convergence criteria is fulfilled [53].
Some examples of Evolutionary algorithms are Simulated annealing algorithm, Tabu search,
Swarm intelligence. The most successful among evolutionary algorithms are Genetic
Algorithms (GAs). They have been investigated by John Holland in 1975, and demonstrate essential
effectiveness [40]. Wrapper and filter feature selection procedure are depicted in Figure 2.
In 2009 Maugis et al. performed variable selection for cluster analysis with Gaussian mixture
models. Here in this study, the model does not need any prior assumptions about the link between the
selected and discarded variables [54]. Ai-Jun and Xin-Yuan proposed a Bayesian stochastic feature
selection approach for gene selection based on regression coefficients using simulation based
Markov chain Monte Carlo methods for Leukemia Colon dataset [55]. Ji et al. presented a novel
method named Partial Least Squares (PLS) based gene-selection method [56]. This method is
suitable for multicategory classification of microarray data. Sharma et al. proposed the algorithm that
first divides genes into a relatively small subset of size h, selects informative smaller subsets of
genes (of size r < h) from a subset and merge the chosen genes with another gene subset (of size r) to
update the gene subset and repeat this process until all subsets are merged into one informative
subset. The effectiveness of the proposed algorithm was shown by analyzing three distinct gene
expression data sets and show the relevance of the selected genes in terms of their biological
functions [57]. Cadenas et al. proposed an approach, which was based on a Fuzzy Random Forest
and it integrates filter and wrapper methods into a sequential search procedure that improved
classification accuracy with the selected features [58]. This new method of feature selection can
handle both crisp and low quality data. The performance of the filter versus wrapper gene selection
technique was evaluated by Srivastava et al. in 2014 by supervised classifiers over three well known
public domain datasets with Ovarian Cancer, Lymphomas & Leukemia [59]. In the study, Relief F
method was used as a filter based gene selection, and Random gene subset selection algorithm was
used as a wrapper based gene selection. Recently, Kar et al. developed a computationally efficient
but accurate gene identification technique “A particle swarm optimization based gene identification
technique” [60]. In this technique at the onset, the t-test method has been utilized to reduce the
dimension of the dataset and then the proposed particle swarm optimization based approach has been
employed to find useful genes.
Figure 2. Feature selection procedure of filter and wrapper approaches [39].

187
AIMS Bioengineering Volume 4, Issue 2, 179-197.
Wrappers tend to perform better, in selecting features because they take the model hypothesis
into account by training and testing in the feature space. The main disadvantage of Wrapper methods
was the number of iterations required to obtain the best feature subset. For every subset evaluation,
the predictor creates a new model, i.e. the predictor was trained for every subset and tested to obtain
the classifier accuracy. If the number of samples were large, most of the algorithm execution time
was spent in training the predictor. Another drawback of using the classifier performance as the
objective function was that the classifiers were prone to overfitting. Overfitting occurs if the
classifier model, well learned the data and provides poor generalization capability. The classifier can
introduce bias and increases the classification error. Using classification accuracy in feature subset
selection, can result in a bad feature subset with high accuracy, but poor generalization power [51].
In the next section, we will discuss embedded methods which try to compensate for the
drawbacks in the Filter and Wrapper methods.
3.1.3. Embedded methods
Embedded methods are different from filter and wrapper in the sense that they still allow
interactions with the learning algorithm for feature selection but the computational time is smaller
than wrapper methods. An embedded method reduces the computation time taken up for
reclassifying different selected subsets in wrapper methods. The main approach of embedded method
is to incorporate the feature selection as part of the training process. Embedded methods attempt to
compensate, for the disadvantages in the filter and wrapper methods. It considers not only relations
between one input features and the output feature, but also searches locally for features that allow
better local discrimination [39]. It uses the independent criteria to decide the optimal subsets for a
known group. After that, the learning algorithm is used to select the final optimal subset among the
optimal subsets of different groups [61]. This embedded method can be roughly categorized into
three, namely pruning method, built-in mechanism and regularization models. In the pruning based
method, initially all the features are taken into the training process for building the classification
model and the features which have less correlation coefficient value are removed recursively using
the support vector machine (SVM). In the built-in mechanism-based feature selection method, a part
of the training phase of the C4.5 and ID3 supervised learning algorithms are used to select the
features. In the regularization method, fitting errors are minimized using the objective functions and
the features with near zero regression coefficients are eliminated [40].
Numerous feature selection techniques have been proposed and found wide applications in
genomics and proteomics. Niijima and Okuno proposed an unsupervised feature selection method,
called LLDA based Recursive Feature Elimination (LLDA-RFE) and applied to several data sets of
cancer microarrays. It performs much better than Fisher score for some of the data sets, despite the
fact that LLDA-RFE is fully unsupervised [62]. Cai et al. proposed a new L2,1-norm SVM, to
naturally select features for multi-class without bothering further heuristic strategy [63]. Maldonado
et al. introduce an embedded method that simultaneously selects relevant features during classifier
construction by penalizing each feature’s use in the dual formulation of support vector
machines (SVM). This approach called kernel-penalized SVM (KP-SVM). It optimizes the shape of
an anisotropic RBF Kernel eliminating features that have low relevance for the classifier [64]. Xiang
et al. present a framework of discriminative least squares regression (LSR) for multiclass
classification and feature selection [65]. Some of the researcher (Liang et al.) integrate multiple data

188
AIMS Bioengineering Volume 4, Issue 2, 179-197.
sources and describe the Multi-Source k-Nearest Neighbor (MS-k NN) algorithm for function
prediction, which finds k-nearest neighbors of a query protein based on different types of similarity
measures and predicts its function by weighted averaging of its neighbors’ functions [66]. Cao et al.
proposed a novel fast feature selection method based on multiple Support Vector Data
Description (SVDD) and applies it to multi-class microarray data [67]. Recursive feature
elimination (RFE) scheme with multiple SVDD was introduced to iteratively remove irrelevant
features, so the proposed method was called multiple MSVDD-RFE [68]. Recently Miron Bartosz
Kursa investigated an idea of incorporating all relevant selections within the training process by
producing importance for implicitly generated shadows, attributes irrelevant by design [69]. General
properties of embedded algorithm are shown in Table 3.
Table 3. Advantages and disadvantages of embedded methods.
Model search Advantages Disadvantages Examples
Embedded
Interacts with the classifier
The models feature dependencies better
computational complexity than the wrapper
Higher performance, accuracy than filter
Less prone to over-fitting than wrapper
Preserving data characteristics for
interpretability
Classifier dependent
selection
Consider the dependence
among features
Decision trees
Weighted naive Bayes
Feature selection using the weight vector of SVM
Random forests
Least absolute shrinkage and selection
operator (LASSO)
Feature selection is very popular in the field of microarray data analysis, because of conceptual
simplicity. However, it presents two major drawbacks. First, a large part of the information contained
in the data set gets lost, since most genes are eliminated by the procedure. Secondly, interactions and
correlations between variables are almost always ignored. A few sophisticated procedures try to
overcome this problem by selecting optimal subsets with respect to a given criterion instead of
filtering out the apparently uninteresting variables. However, these methods generally suffer from
over fitting. The obtained variable subsets might be optimal for the learning data set but do not
perform nicely on independent test data. Moreover, they are based on computationally intensive
iterative algorithms and thus very difficult to implement and interpret.
3.2. Feature extraction
Feature extraction is an intelligent substitute to feature selection to reduce the size of high-
dimensional data. In the literature, it is also called “Feature construction” or “projection onto a low
dimensional subspace”. Feature extraction method transforms the original feature in the lower
dimensional space, in this way the problem is represented in a more discriminating (informative)
space that makes the further analysis more efficient. There are two main types of feature extraction
algorithms, linear and nonlinear. Linear methods are usually faster, more robust and more
interpretable than non-linear methods. On the other hand, non-linear methods can sometimes
discover for the complicated structures of data (e.g. embedment’s) where linear methods fail to
distinguish [70].

189
AIMS Bioengineering Volume 4, Issue 2, 179-197.
3.2.1. Linear feature extraction
Linear feature extraction assumes that the data are linearly separable in a lower-dimensional
subspace. It transforms them on this subspace by using matrix factorization method. Given a dataset
X:N.D, there exists a projection matrix U:D.K and a projection Z:N.K, where Z = X × U. Using
UUT = I (orthogonal property of eigenvectors), we get X = Z × UT [71]. The most famous linear
feature extraction method is principal component analysis (PCA). PCA uses the covariance matrix
and its eigenvalues and eigenvectors, to find the “principal components” in the data that are
uncorrelated eigenvectors, each demonstrating some proportion of variance in the data. PCA and its
several versions have been applied to reduce the dimensionality of the cancer microarray data. These
methods were highly effective in identifying important features of the data. PCA cannot easily
capture nonlinear relationship that frequently exists in high dimensional data, especially in complex
biological systems, this is the main drawback of PCA [72]. Classical multidimensional
scaling (classical MDS) or Principal Coordinates Analysis that estimates the matrix of dissimilarities
for any given matrix input are the similar linear approach for data extraction. It was used for high
dimensional gene expression datasets because it is effective in combination with Vector Quantization
or K-Means that assigns each observation to a class, from the total of K classes [73].
3.2.2. Nonlinear feature extraction
Nonlinear feature extraction works in different ways for dimensionality reduction. In general
kernel functions can be considered to create the same effect without using any type of lifting
function [71]. Kernel PCA is an important nonlinear method of feature extraction for classification. It
has been widely used for biological data. Since, dimensionality reduction helps in the understanding
of the results [72]. Nonlinear feature extraction using Manifolds is another similar approach for
dimensional reduction. It has been built on the hypothesis that the data (genes of interest) lie on an
embedded nonlinear manifold that has lower dimension than the raw data space and lies within it.
Many methods exist working in the manifold space and applied to reduce the dimension of
microarrays, such as Locally Linear Embedding (LLE) and Laplacian Eigenmaps [74]. Kernel PCA
and extraction using manifold methods are widely used feature extraction method for dimension
reduction of the microarray. Self-organizing maps (SOM) can also be used for reducing the
dimension of gene expression data but it was never generally accepted for analysis. As, it needs just
the accurate amount of data to implement well [70]. SOM can often be better separated using
manifold LLE but kernel PCA is far faster than the other two. Kernel PCA has an important
limitation in terms of space complexity since it stores all the dot products of the training set and
therefore, the size of the matrix increases quadratically with the number of data points. Independent
component analysis (ICA) is also widely used in microarrays [75]. Independent component
analysis (ICA) is a Feature extraction technique, which was proposed by Hyvarinen to solve the
typical problem of the non-Gaussian processes and has been applied successfully in different fields.
The extraction process of ICA is very similar to the algorithm of PCA. PCA maps the data into
another space with the help of principal component. In place of Principal component, the ICA
algorithm finds the linear representation of non-Gaussian data so that the extracted components are
statistically independent [76,77,78]. ICA finds the correlation among the data and decorrelates the
data by maximizing or minimizing the contrast information. This is called “whitening”. The

190
AIMS Bioengineering Volume 4, Issue 2, 179-197.
whitened matrix is then rotated to minimize the Gaussianity of the projection and in effect retrieve
statistically independent data. ICA can also be applied in combination with PCA and combination
work better than individuals. This could simply be due to the decrease in computational load caused
by the high dimension [78]. Popular feature extraction techniques with their properties are shown in
Table 4.
Table 4. Advantages and disadvantages of feature extraction methods.
Model search Advantages Disadvantages Examples
Extraction
Higher discriminating power
Control over fitting problem
Loss data interpretability
The transformation may be expensive
PCA
Linear discriminant analysis
ICA
Feature extraction control over fitting compared to feature selection when it is unsupervised.
Extracted features have the higher discriminating power which gives good classification accuracy.
But sometimes data interpretability is lost after transformation and the process of transformation may
be costly for a different type of datasets [27].
3.3. Hybrid methods for dimension reduction
Recently, a hybrid search technique has been used for dimension reduction that was proposed
by Huang et al. in 2007, that has the advantages of the both filter/extraction and the wrapper
method [79]. A hybrid dimension reduction technique consists two stages, in the first step, a
filter/extraction method are used to identify best relevant features of the data sets. In the second step,
which constitutes a wrapper method, verifies the previously identified relevant feature subsets are
verified by a method that gives higher classification accuracy rates [80,81]. It uses different
evaluation criteria in different search stages, to improve the efficiency and classification accuracy
with better computational performance [21]. In the hybrid search algorithm, the first subset of
features is selected or extracted based on the filter/extraction method and after that the wrapper
method is used to select the final feature set. Therefore the computational cost of the wrapper method
becomes acceptable due to the use of reduced size features [71]. ICA and fuzzy algorithm [76],
Information gain and a Memetic algorithm [82], Fishers core with a GA and PSO [83], mRAR with
ABC algorithm [84] have recently used the hybrid method to solve the problem of dimensionality
reduction of the microarray. Advantages of hybrid methods are listed in Table 5.
Table 5. Advantages and disadvantages of hybrid methods.
Model search Advantages Disadvantages
Hybrid
Higher performance, accuracy than filter
Better computational complexity than wrapper
More flexible and robust upon high dimensional data
Classifier specific methods
Dependents of the combination of
different feature selection method
Minimal redundancy and maximum relevancy (mRMR) is the most popular feature selection
method used as a component of the combination with different wrapper methods. For example, Hu
et al. applied the search strategy of mRMR for constructing neighborhood mutual information (NMI)

191
AIMS Bioengineering Volume 4, Issue 2, 179-197.
for improving the efficiency of mRMR gene selection and to evaluate the relevance between genes
and related decision [85]. Akadi et al. propose a two-stage gene selection by combining mRMR as a
filter and genetic algorithm (GA) as wrapper [86]. Shreem et al. used Relief F and mRMR as filter
stage to minimize redundancy and GA with a classifier to choose the most discriminating genes [87].
Zibakhsh et.al. detected genes using information gain a novel memetic algorithm with a multi-view
fitness function [82]. Alshamlan et al. used mRMR-ABC as a hybrid gene selection algorithm for
cancer classification of microarray [84]. A hybrid meta-heuristic algorithm called Genetic Bee
Colony (GBC) algorithm which combines the advantages of two naturally inspired algorithms:
Genetic Algorithm (GA) and Artificial Bee Colony (ABC) is explained by Alshamlan et al. [88]. A
complete balance between exploitation and exploration is needed for meta-heuristic population based
algorithms. Few modifications are done in the basic ABC and GA algorithm to enhance their abilities.
The experiments on various binary and multi class datasets of microarray showed that GBC as a
hybrid algorithm selects few genes with high classification accuracy and also when compared with
other algorithms like ABC, mRMR-ABC, mRMR-PSO, mRMR-GA, GBC gives better results [39].
A general framework of embedded algorithm and hybrid algorithm are shown in Figure 3.
Figure 3. Feature selection mechanism of embedded and hybrid approaches.
Chuang et al. proposed to combine Tabu search (TS) and Binary Particle Swarm
Optimization (BPSO) for feature selection [89]. BPSO acts as a local optimizer each time the TS
have been run for a single generation. The K-nearest neighbor method with leave one out cross
validation and support vector machine with one versus rest serve as evaluators of the TS and BPSO.
Hybrid genetic algorithms (GA) and artificial neural networks (ANN) are not new in the machine
learning culture. Tong and Mintram proposed this method [90]. Such hybrid systems have been
shown to be very successful in classification and prediction problems. The widely used k top scoring
pair (k-TSP) algorithm is a simple yet powerful parameter free classifier. It owes its success in many
cancer microarray data sets to an effective feature selection algorithm that is based on the relative
expression ordering of gene pairs. Shi et al. proposed this method in 2011 and is used to predict
Cancer outcome [91]. The top scoring pairs generated by the k-TSP ranking algorithm can be used as
a dimensionally reduced subspace for other machine learning classifiers. A feature selection method
based on sensitivity analysis and the fuzzy Interactive Self Organizing Data Algorithm (ISODATA)
is proposed by Liu et al. for selecting features from high dimensional gene expression data sets [92].

192
AIMS Bioengineering Volume 4, Issue 2, 179-197.
Hajiloo et al. introduces fuzzy support vector machine which is a learning algorithm based on the
combination of fuzzy classifiers and kernel machines for microarray classification [93]. Chang et al.
applied a hybrid of feature selection and machine learning methods in oral cancer prognosis based on
the parameters of the correlation of clinicopathologic and genomic markers [94].
To address the drawbacks of each filtering method and wrapper method several hybrid
algorithms have been proposed for a microarray data in the literature with suitable results. The
Hybrid method gives the best performance for different machine learning classification algorithm
than the filter methods and better computational complexity and least prone to overfitting than the
wrapper methods. But the performance of hybrid methods are totally dependent on the choice of the
classifier, and the combination of the different filter and the wrapper approach.
4. Conclusion
This paper has presented two different ways of reducing the dimensionality of high dimensional
microarray data. The first is to select the best features from the original feature set this is called
feature selection. On the other hand, feature extraction methods transform the original features into a
lower dimensional space by using linear or a nonlinear combination of the original features. To
analyze microarray data, dimensionality reduction methods is essential in order to get meaningful
results. In this whole paper different aspects feature selection and extraction methods were described
and compared. The advantage and disadvantage of these methods are streamlined to get the clear
idea about, when to use which method, in order to save computational time and resources. In
addition, we have also described a hybrid method that incorporates increasing the classifier accuracy
and reducing the computational complexity of an existing method.
Acknowledgement
The author would like to acknowledge the support of the Director, Maulana Azad National
Institute of Technology, Bhopal-462003 (M.P.), India, for providing basic facilities in the institute.
Conflict of Interest
All authors declare no conflict of interest.
Reference
1. Chang TW (1983) Binding of cells to matrixes of distinct antibodies coated on solid surface. J
Immunol Methods 65: 217–223.
2. Lenoir T, Giannella E (2006) The emergence and diffusion of DNA microarray technology. J
Biomed Discov Collab 1: 11–49.
3. Pirrung MC, Read LJ, Fodor SPA, et al. (1992) Large scale photolithographic solid phase
synthesis of polypeptides and receptor binding screening thereof: US, US5143854[P].
4. Peng S, Xu Q, Ling XB, et al. (2003) Molecular classification of cancer types from microarray
data using the combination of genetic algorithms and support vector machines. Febs Lett 555:
358–362.

193
AIMS Bioengineering Volume 4, Issue 2, 179-197.
5. Statnikov A, Aliferis CF, Tsamardinos I, et al. (2005) A comprehensive evaluation of
multicategory classification methods for microarray gene expression cancer diagnosis.
Bioinformatics 21: 631–643.
6. Tan Y, Shi L, Tong W, et al. (2005) Multi-class cancer classification by total principal
component regression (TPCR) using microarray gene expression data. Nucleic Acids Res 33: 56–
65.
7. Eisen MB, Brown PO (1999) DNA arrays for analysis of gene expression. Method Enzymol 303:
179–205.
8. Leng C (2008) Sparse optimal scoring for multiclass cancer diagnosis and biomarker detection
using microarray data. Comput Biol Chem 32: 417–425.
9. Quackenbush J (2001) Computational analysis of microarray data. Nat Rev Genet 2: 418–427.
10. Piatetsky-Shapiro G, Tamayo P (2003) Microarray data mining: facing the challenges. ACM
Sigkdd Explor Newslett 5: 1–5.
11. Eisen MB, Spellman PT, Brown PO, et al. (1998) Cluster analysis and display of genome-wide
expression patterns. P Natl Acad Sci USA 95: 14863–14868.
12. Golub TR, Slonim DK, Tamayo P, et al. (1999) Molecular classification of cancer: class
discovery and class prediction by gene expression monitoring. Science 286: 531–537.
13. O'Neill MC, Song L (2003) Neural network analysis of lymphoma microarray data: prognosis
and diagnosis near-perfect. Bioinformatics 4: 1–12.
14. Beer DG, Kardia SL, Huang CC, et al. (2002) Gene-expression profiles predict survival of
patients with lung adenocarcinoma. Nat Med 8: 816–824.
15. Lee JW, Lee JB, Park M, et al. (2005) An extensive comparison of recent classification tools
applied to microarray data. Comput Stat Data An 48: 869–885.
16. You W, Yang Z, Yuan M, et al. (2014) Totalpls: local dimension reduction for multicategory
microarray data. IEEE T Hum Mach Syst 44: 125–138.
17. Xi M, Sun J, Liu L, et al. (2016) Cancer feature selection and classification using a binary
quantum-behaved particle swarm optimization and support vector machine. Comput Math
Method Med 2016: 1–9.
18. Wang L, Feng Z, Wang X, et al. (2010) DEGseq: an R package for identifying differentially
expressed genes from RNA-seq data. Bioinformatics 26: 136–138.
19. Shen Q, Mei Z, Ye BX (2009) Simultaneous genes and training samples selection by modified
particle swarm optimization for gene expression data classification. Comput Biol Med 39: 646–
649.
20. Xie J, Xie W, Wang C, et al. (2010) A novel hybrid feature selection method based on ifsffs and
svm for the diagnosis of erythemato-squamous diseases.J Mach Learn Res 11: 142–151.
21. Chuang LY, Yang CH, Wu KC, et al. (2011) A hybrid feature selection method for DNA
microarray data. Comput Biol Med 41: 228–237.
22. Li B, Zheng CH, Huang DS, et al. (2010) Gene expression data classification using locally linear
discriminant embedding. Comput Biol Med 40: 802–810.
23. Mahajan S, Singh S (2016) Review on feature selection approaches using gene expression data.
IJIR 2: 356–364.
24. Pinkel D, Segraves R, Sudar D, et al. (1998) High resolution analysis of DNA copy number
variation using comparative genomic hybridization to microarrays. Nat Genet 20: 207–211.

194
AIMS Bioengineering Volume 4, Issue 2, 179-197.
25. Cheadle C, Vawter MP, Freed WJ, et al. (2003) Analysis of microarray data using Z score
transformation. J Mol Diagn 5: 73–81.
26. Witten IH, Frank E (2016) Data mining: practical machine learning tools and techniques, 4th
Edition, Morgan Kaufmannis, 4–7.
27. Dubitzky W, Granzow M, Berrar D (2002) Data mining and machine learning methods for
microarray analysis, In: Methods of microarray data analysis, Springer US, 5–22.
28. Brown MP, Grundy WN, Lin D, et al. (2000) Knowledge-based analysis of microarray gene
expression data by using support vector machines. P Natl Acad Sci USA 97: 262–267.
29. Dudoit S, Fridlyand J, Speed TP (2002) Comparison of discrimination methods for the
classification of tumors using gene expression data. J Am Stat Assoc 97: 77–87.
30. Khan J, Wei JS, Ringner M, et al. (2001) Classification and diagnostic prediction of cancers
using gene expression profiling and artificial neural networks. Nat Med 7: 673–679.
31. Zheng CH, Huang DS, Shang L (2006) Feature selection in independent component subspace for
microarray data classification. Neurocomputing 69: 2407–2410.
32. Peng Y (2006) A novel ensemble machine learning for robust microarray data classification.
Comput Biol Med 36: 553–573.
33. Mohan A, Rao MD, Sunderrajan S, et al. (2014) Automatic classification of protein structures
using physicochemical parameters. Interdiscipl Sci 6: 176–186.
34. Saeys Y, Inza I, Larrañaga P (2007) A review of feature selection techniques in bioinformatics.
Bioinformatics 23: 2507–2517.
35. Jirapech-Umpai T, Aitken S (2005) Feature selection and classification for microarray data
analysis: Evolutionary methods for identifying predictive genes. Bioinformatics 6: 148–148.
36. Law MH, Figueiredo MA, Jain AK (2004) Simultaneous feature selection and clustering using
mixture models. IEEE T Pattern Anal 26: 1154–1166.
37. Lazar C, Taminau J, Meganck S, et al. (2012) A survey on filter techniques for feature selection
in gene expression microarray analysis. IEEE/ACM T Comput Biol Bioinform 9: 1106–1119.
38. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res
3: 1157–1182.
39. Ang JC, Mirzal A, Haron H, et al. (2015) Supervised, unsupervised and semi-supervised feature
selection: a review on gene selection. IEEE/ACM T Comput Biol Bioinform 13: 971–989.
40. Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng 40:
16–28.
41. Lin KS, Chien CF (2009) Cluster analysis of genome-wide expression data for feature extraction.
Expert Syst Appl 36: 3327–3335.
42. Sun Y, Todorovic S, Goodison S (2010) Local-learning-based feature selection for high-
dimensional data analysis. IEEE T Pattern Anal 32: 1610–1626.
43. Zhu S, Wang D, Yu K, et al. (2010) Feature selection for gene expression using model-based
entropy. IEEE/ACM T Comput Biol Bioinform 7: 25–36.
44. Mishra D, Sahu B (2011) Feature selection for cancer classification: a signal-to-noise ratio
approach. IJSER 2: 1–7.
45. Wei D, Li S, Tan M (2012) Graph embedding based feature selection. Neurocomputing 93: 115–
125.
46. Liu JX, Wang YT, Zheng CH, et al. (2013) Robust PCA based method for discovering
differentially expressed genes. BMC bioinform 14: S3.

195
AIMS Bioengineering Volume 4, Issue 2, 179-197.
47. Maulik U, Chakraborty D (2014) Fuzzy preference based feature selection and semisupervised
SVM for cancer classification. IEEE T Nano Biosci 13: 152–160.
48. Chinnaswamy A, Srinivasan R (2016) Hybrid feature selection using correlation coefficient and
particle swarm optimization on microarray gene expression data. IBICA, 229–239.
49. Mortazavi A, Moattar MH (2016) Robust feature selection from microarray data based on
cooperative game theory and qualitative mutual information. Adv Bioinform 2016: 1–16.
50. John GH, Kohavi R, Pfleger K (1994) Irrelevant features and the subset selection problem, In:
machine learning, Proceedings of the Eleventh International Conference, 121–129.
51. Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intel 97: 273–324.
52. Somol P, Pudil P, Novovičová J, et al. (1999) Adaptive floating search methods in feature
selection. Pattern Recogn Lett 20: 1157–1163.
53. Youssef H, Sait SM, Adiche H (2001) Evolutionary algorithms, simulated annealing and tabu
search: a comparative study. Eng Appl Artif Intel 14: 167–181.
54. Maugis C, Celeux G, Martin-Magniette ML (2009) Variable selection for clustering with
Gaussian mixture models. Biometrics 65: 701–709.
55. Ai-Jun Y, Xin YS (2010) Bayesian variable selection for disease classification using gene
expression data. Bioinformatics 26: 215–222.
56.Ji G, Yang Z, You W (2011) PLS-based gene selection and identification of tumor-specific genes.
IEEE T Syst Man Cy C 41: 830–841.
57. Sharma A, Imoto S, Miyano S (2012) A top-r feature selection algorithm for microarray gene
expression data. IEEE/ACM T Comput Biol Bioinform 9: 754–764.
58. Cadenas JM, Garrido MC, MartíNez R (2013) Feature subset selection filter–wrapper based on
low quality data. Expert Syst Appl 40: 6241–6252.
59. Srivastava B, Srivastava R, Jangid M (2014) Filter vs. wrapper approach for optimum gene
selection of high dimensional gene expression dataset: an analysis with cancer datasets. IEEE
High Perform Comput Appl 454: 1–6.
60. Kar S, Sharma KD, Maitra M (2016) A particle swarm optimization based gene identification
technique for classification of cancer subgroups. IEEE Control Instrum Energ Commun, 130–134.
61. Kumar V, Minz S (2014) Feature selection:a literature review. Smart Cr 4: 211–229.
62. Niijima S, Okuno Y (2009) Laplacian linear discriminant analysis approach to unsupervised
feature selection. IEEE/ACM T Comput Biol Bioinform 6: 605–614.
63. Cai X, Nie F, Huang H, et al. (2011) Multi-class l2, 1-norm support vector machine. IEEE
Comput Soc, 91–100.
64. Maldonado S, Weber R, Basak J (2011) Simultaneous feature selection and classification using
kernel-penalized support vector machines. Inform Sci Int J 181: 115–128.
65. Xiang S, Nie F, Meng G, et al. (2012) Discriminative least squares regression for multiclass
classification and feature selection. IEEE T Neur Netw Learn Syst 23: 1738–1754.
66. Lan L, Djuric N, Guo Y, et al. (2013) MS-kNN: protein function prediction by integrating
multiple data sources. Bioinformatics 14: S8.
67. Cao J, Zhang L, Wang B, et al. (2015) A fast gene selection method for multi-cancer
classification using multiple support vector data description. J Biomed Inform 53: 381–389.
68. Lan L, Vucetic S (2011) Improving accuracy of microarray classification by a simple multi-task
feature selection filter. Int J Data Min Bioinform 5: 189–208.

196
AIMS Bioengineering Volume 4, Issue 2, 179-197.
69. Kursa MB (2016) Embedded all relevant feature selection with random ferns. arXiv preprint
arXiv: 1604.06133.
70. Bartenhagen C, Klein HU, Ruckert C, et al. (2010) Comparative study of unsupervised
dimension reduction techniques for the visualization of microarray gene expression data.
Bioinformatics 11: 567–577.
71. Kotsiantis S (2011) Feature selection for machine learning classification problems: a recent
overview. Artif Intell Rev, 1–20.
72. Hira ZM, Gillies DF (2015) A review of feature selection and feature extraction methods applied
on microarray data. Adv Bioinform 2015: 1–13.
73. Tzeng J, Lu HH, Li WH (2008) Multidimensional scaling for large genomic data sets.
Bioinformatics 9: 179–195.
74. Ehler M, Rajapakse VN, Zeeberg BR, et al. (2011) Nonlinear gene cluster analysis with labeling
for microarray gene expression data in organ development. BMC proc 5: S3.
75. Kong W, Vanderburg CR, Gunshin H, et al. (2008) A review of independent component analysis
application to microarray gene expression data. Biotechniques 45: 501–520.
76. Aziz R, Verma C, Srivastava N (2016) A fuzzy based feature selection from independent
component subspace for machine learning classification of microarray data. Genom Data 8: 4–15.
77. Hsu CC, Chen MC, Chen LS (2010) Integrating independent component analysis and support
vector machine for multivariate process monitoring. Comput Ind Eng 59: 145–156.
78. Naik GR, Kumar DK (2011) An overview of independent component analysis and its
applications. Informatica 35: 63–81.
79. Huang Y, Lowe HJ (2007) A novel hybrid approach to automated negation detection in clinical
radiology reports. J Am Med Inform Assoc 14: 304–311.
80. Aziz R, Verma C, Srivastava N (2015) A weighted-SNR feature selection from independent
component subspace for nb classification of microarray data. Int J Adv Biotec Res 6: 245–255.
81. Aziz R, Srivastava N, Verma C (2015) T-independent component analysis for svm classification
of dna-microarray data. Int J Bioinform Res 6: 305–312.
82. Zibakhsh A, Abadeh MS (2013) Gene selection for cancer tumor detection using a novel
memetic algorithm with a multi-view fitness function. Eng Appl Artif Intel 26: 1274–1281.
83. Zhao W, Wang G, Wang Hb, et al. (2011) A novel framework for gene selection. Int J Adv
Comput Technol 3: 184–191.
84. Alshamlan H, Badr G, Alohali Y (2015) mRMR-ABC: a hybrid gene selection algorithm for
cancer classification using microarray gene expression profiling. Bio Med Res Int 2015: 1–15.
85. Hu Q, Pan W, An S, et al. (2010) An efficient gene selection technique for cancer recognition
based on neighborhood mutual information. Int J Mach Learn Cybern 1: 63–74.
86. El Akadi A, Amine A, El Ouardighi A, et al. (2011) A two-stage gene selection scheme utilizing
MRMR filter and GA wrapper. Knowl Inform Syst 26: 487–500.
87. Shreem SS, Abdullah S, Nazri MZA, et al. (2012) Hybridizing ReliefF, MRMR filters and GA
wrapper approaches for gene selection. J Theor Appl Inform Technol 46: 1034–1039.
88. Alshamlan HM, Badr GH, Alohali YA (2015) Genetic bee colony (GBC) algorithm: a new gene
selection method for microarray cancer classification. Comput Bio Chem 56: 49–60.
89. Chuang LY, Yang CH, Yang CH (2009) Tabu search and binary particle swarm optimization for
feature selection using microarray data. J Comput Biol 16: 1689–1703.

197
AIMS Bioengineering Volume 4, Issue 2, 179-197.
90. Tong DL, Mintram R (2010) Genetic algorithm-neural network (GANN): a study of neural
network activation functions and depth of genetic algorithm search applied to feature selection.
Int J Mach Learn Cybern 1: 75–87.
91. Shi P, Ray S, Zhu Q, et al. (2011) Top scoring pairs for feature selection in machine learning and
applications to cancer outcome prediction. Bioinformatics 12: 375–399.
92. Liu Q, Zhao Z, Li YX, et al. (2012) Feature selection based on sensitivity analysis of fuzzy ISO
data. Neurocomputing 85: 29–37.
93. Hajiloo M, Rabiee HR, Anooshahpour M (2013) Fuzzy support vector machine: an efficient rule-
based classification technique for microarrays. Bioinformatics 14: S4.
94. Chang SW, Abdul-Kareem S, Merican AF, et al. (2013) Oral cancer prognosis based on
clinicopathologic and genomic markers using a hybrid of feature selection and machine learning
methods. Bioinformatics 14: 1–15.
© 2017 Rabia Aziz, et al., licensee AIMS Press. This is an open
access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/4.0)