REVIEW ON FEATURE SELECTION TECHNIQUES
AND THE IMPACT OF SVM FOR CANCER
CLASSIFICATION USING GENE EXPRESSION PROFILE
1
1

G.Victo Sudha George and 2Dr. V.Cyril Raj

Asst.Professor ,Dept of CSE , Dr.M.G.R Universiy,Chennai-95 ,India
sudhajose72@yahoo.com
2

Professor& Head ,Dr. M.G.R University ,Chennai -95,India

ABSTRACT
The DNA microarray technology has modernized the approach of biology research in such a way that
scientists can now measure the expression levels of thousands of genes simultaneously in a single experiment.
Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a
medical diagnosis tool. But compared to the number of genes involved, available training data sets generally
have a fairly small sample size for classification. These training data limitations constitute a challenge to
certain classification methodologies. Feature selection techniques can be used to extract the marker genes
which influence the classification accuracy effectively by eliminating the un wanted noisy and redundant
genes This paper presents a review of feature selection techniques that have been employed in micro array
data based cancer classification and also the predominant role of SVM for cancer classification.

KEYWORDS
Microarray, feature selection, cancer classification, integrative gene selection.

1. INTRODUCTION
DNA micro array is a prominent high throughput technology that allows the expression levels of
thousands of genes to be monitored simultaneously. Today, the analysis of gene expression data is
one of the major topics in health informatics [1]. For instance, the classification of DNA micro array
data allows the discovery of hidden patterns in expression profiles and opened possibility for
accurate cancer classification.
The main challenge in classifying gene expression data is the curse of dimensionality problem.
There is large number of genes (features) compared to small sample sizes [2,3]. To overcome this,
feature selection is used to identify differentially expressed genes and to remove irrelevant genes.
Gene selection remains as an critical task to improve the accuracy and speed of classification
systems[4].In general, feature selection can be organized into three categories: filter, wrapper and
embedded methods. They are categorized based on how a feature selection technique combines with
the construction of a classification model. A considerable amount of literature has been published
on gene selection methods for building effective classification model. In this paper we present a
review of feature selection techniques for cancer classification and also the predominant role of
SVM for cancer classification.

2. DNA MICROARRAY
Microarrays offer an efficient method of gathering data that can be used to determine the expression
pattern of thousands of genes. The mRNA expression pattern from different tissues in normal and
diseases states could reveal which genes and environmental conditions can lead to disease. The
experimental steps of typical microarray began with extraction of mRNA from a tissues sample or
probe. The mRNA is then labeled with fluorescent nucleotides, eventually yielding fluorescent
(typically red) cDNA. The sample later is incubated with similarly processed cDNA reference
(typically green). The labeled probe and reference are then mixed and applied to the surface of
DNA microarrays, allowing fluorescent sequences in the probe-reference mix to attach to the cDNA
adherent to the glass slide. The attraction of labeled cDNA from the probe and reference for a
particular spot on microarray depends on the extent to which the sequences in the mix (probe reference) complement the DNA affixed to the slide. A perfect compliment, in which a nucleotide
sequence on a strand of cDNA exactly matches a DNA sequence affixed to the slide, is known as
hybridization. Hybridization is the key element in microarray technology. The populated microarray
is then excited by a laser and the consequential fluorescent at each spot in the microarray is
measured. If neither the probe nor the reference samples hybridize with the gene spotted on the
slide, the spot will appear in the black color. However, if hybridization is predominantly with the
probe, the spot will be in red (Cy5). Conversely, if hybridization is primarily between the reference
and DNA affixed to the slide, the spot will fluoresce green (Cy3). The spot can also incandescent
yellow, when cDNA from probe and reference samples hybridize equally at a given spot, indicating
that they share the same number of complementary nucleotides in particular spot. Using image
processing software, the red-to-green fluorescence will be digitized and providing the ratio values
output indicating the expression of genes. The process of microarray experiment is illustrated in
Figure 1.

Figure 1: Microarray Experiment

Finally, the gene expression data set can be noted by the following matrix M { wij |1 ≤ i ≤ n,1 ≤ j ≤
m}, where the rows (G { g1 ,…,gn } ) from the expression patterns of genes, the columns ( S { s1
,…,sm } ) from the expression profiles of samples, and wij is the measured expression level of gene
i in sample j . Thus, M is defined as:

Due to its high throughput nature, microarray data poses new challenges for data analysis. Although
the type of analysis depends on the research questions posed, typical steps in the analysis of
microarray data are: i) pre-processing and normalization, ii) detection of genes with significant fold
changes, iii) classification and clustering of expression profiles.

3. CHALLENGES FACED IN MICROARRAY DATA ANALYSIS
Many challenges in microarray need to be addressed before new knowledge about gene expression
can be revealed. Some of the problems are:
a. Bias and confounding Problem: which occurred during study, design phase of microarray and can
lead to erroneous conclusion. Technical factors, such as differences in physical, batch of reagents
used and various levels of skill in technician could possibly cause bias. Confounding on the other
hand, take place when another factors distorts the true relationship between the study variables of
interest.
b. Cross-platform comparisons of gene expression studies are difficult to conduct when microarrays
were constructed using different standards. Thus, the results cannot be reproduced. To deal with this
problem, Minimal Information about a Microarray Experiment (MIAME) [5] has been developed to
improve reproducibility, sensitivity and robustness in gene expression analysis.
c. Microarray data is high dimensional data characterized by thousand of genes in few sample sizes,
which cause significant problems such as irrelevant and noise genes, complexity in constructing
classifiers, and multiple missing gene expression values due to improper scanning. Moreover, most
of studies that applied microarray data are suffered from data over fitting, which requires additional
validation.
d. Mislabeled data or questioned tissues result by experts also another types of drawback that could
decrease the accuracy of experimental results and led to imprecise conclusion about gene expression
patterns.
e. Biological relevancy result is another integral criterion that should be taken into account in
analyzing microarray data rather than only focusing on accuracy of cancer classification. Although
there is no doubt gaining high accuracy classification results are important in microarray data

analysis, but revealing the biological information during the process of cancer classification is also
essential. For instance determination of genes that are under expressed or over expressed in
cancerous cells could assists domain experts in designing and planning more appropriate treatments
for cancer patients. Therefore, most of domain experts are interested in classifiers that not only
produce high classification accuracy but also reveal important biological information.

4. FEATURE SELECTION TECHNIQUES IN MICRO ARRAY DATA ANALYSIS
DNA micro array technology is used to measure changes in expression levels of genes. This
expression of the genetic information occurs in two stages: transcription stage and translation stage.
In transcription, DNA molecules are transcribed into mRNA while in translation stage, mRNA is
translated to amino acid sequences of the corresponding proteins. DNA micro array analysis
provides access to thousands of genes at once by recording expression levels simultaneously. It has
been shown that gene expression changes are related with different types of cancers [37]. Cancer
classification using gene expression data is a nontrivial task due to the very nature of the gene
expression data. The expression data has very high dimensionality, usually in the order of thousands
to tens of thousands of genes. The situation is more complicated with the number of sample sizes,
usually below hundred. The high dimensionality of the features and the low population size usually
cause over-fitting of the classifier. A term - the curse of dimensionality, is coined to refer to this
situation. Computational expenses also impose important limitations. Another key issue is, due to
not all genes being related to the cancer, it is difficult to extract biologically meaningful genes.
The Taxonomy of dimensionality reduction techniques can be divided into two categories,
transformation or selection based reduction. The key distinction made within the taxonomy is
whether a dimensionality reduction technique will transform or preserves the dataset semantics in
the process of reduction. Transformation based reduction such as Principal Component Analysis
(PCA) transforms the original features of a dataset with a typically reduced number of uncorrelated
ones, termed principal component. In contrast, selection reduction techniques attempt to determine a
minimal feature subset from a problem domain while retaining the meaning of the original feature
sets. Thus, selection based reduction techniques have become the main preference in many
bioinformatics applications, especially microarray data analysis since it offers the advantage of
interpretability by a domain expert. Feature selection is the process of systematically reducing the
dimensionality of a dataset to an optimal subset of attributes for classification purposes. Problem of
feature selection is hence, an important issue in cancer classification. It has been shown that, in
many applications feature selection process improves a classifier's prediction capability [38].
The objectives of feature selection techniques are many, the major ones are: i. To avoid over fitting
and improve model performance, for example selecting highly informative genes could enhance the
accuracy of classification model. ii. To provide faster and more cost-effective models, and iii. To
gain a deeper insight into the underlying processes that generated the data. Although, feature
selection techniques have many benefits, it also introduces extra complexity level which requires
thoughtful experiment design to address the challenging tasks, yet provide fruitful results. In the
context of classification, feature selection techniques can be organized into three categories,
depending on how they combine the feature selection search with the construction of the
classification model: filter method, wrapper method and embedded method.
Filter method rank each feature according to some univariate metric, and only the highest ranking
features are used while the remaining low ranking features are eliminated. This method also relies
on general characteristics of the training data to select some features without involving any learning
algorithm. Therefore, the results of filter model will not affecting any classification algorithm.
Moreover, filter methods also provide very easy way to calculate and can simply scale to large-

scale microarray datasets since it only have a short running time. Univariate filter methods such as
Bayesian Network [6] Information Gain (IG) and Signal-to-Ratio(SNR) [7][10] and Euclidean Distance
[8][9]
have been extensively used in microarray data to identify informative genes. Information Gain
has been reported to be the superior gene selection technique by [8][11] however different types of
univariate technique appears to be significant when it was trained over various datasets. Bayesian
Networks, on the other hand appear to be the ideal platform for the integration of heterogeneous
sources of information [6]. Beside the application of parametric techniques in determining
informative genes from microarray data [12][3][14] have applied non-parametric technique such as
threshold number of misclassification or TNoM score. This technique basically separate the
informative gene by assigning a threshold value. However, it is hard to determine the most
appropriate threshold. Other nonparametric techniques such as Pearson correlation coefficient [8] [9]
and Significant Analysis of Microarray (SAM) [15] as been reported to be the top feature selection
techniques. Univariate filter methods have been widely utilized in microarray data analysis. This
trend can be clarified by a number of reasons for instance the output the result provide by univariate
gene rankings is intuitive and easy to understand. These simplify version of output could fulfill the
aims and expectations of biology and molecular domain experts who demand for validation of result
using laboratory techniques. In addition, filter methods also offer less computational time to
generate results which is an extra point to be preferred by domain experts. However, gene ranking
based on univariate methods has some drawbacks. The major one is the genes selected are most
probably redundant. This means highly ranked genes may carry similar discriminative information
towards the defined class. Although we eliminate one high ranked gene it may not cause any
degradation of classification accuracy. Since univariate filter methods do not count the relationship
between genes, [16] developed an optimal gene selection method called Markov Blanket Filtering,
which can remove redundant genes to eliminate this problem. Based on this method [17] proposed
the Redundancy Based Filter (RBF) method to deal with redundant problems and the results are
quite promising.
While the filter techniques handle the identification of genes independently, a wrapper method on
the other hand, embeds a gene selection method within a classification algorithm. In the wrapper
methods [18] a search is conducted in the space of genes, evaluating the goodness of each found gene
subset by the estimation of the accuracy percentage of the specific classifier to be used, training the
classifier only with the found genes. It is claimed that the wrapper approach obtains better
predictive accuracy estimates than the filter approach [19] however, its computational cost must be
taken into account. Wrapper methods can be divided into distinct groups, deterministic and
randomized search algorithm. Genetic Algorithm (GA) is a randomized search algorithm and
optimization mimicking evolution and natural genetics. It has been employed for binary and multiclass cancer discrimination in [20][21]. A common drawback of wrapper methods, such as GA is that
they have a higher risk of over-fitting than filter techniques and are very computationally intensive.
In contrast, wrapper methods incorporate the interaction between genes selection and classification
model, which make them unique compared to filter techniques.
The third class of feature selection approaches is embedded methods. The different of embedded
methods with others feature selection methods is the search mechanism is built into the classifier
model. Identical to wrapper methods, embedded methods are therefore specific to a given learning
algorithm. Embedded methods have the advantage that they include the interaction with the
classification model, while at the same time being far less computationally intensive than wrapper
methods. Support Vector Machine (SVM) method of Recursive Feature Elimination (RFE) was
employed in [22] for gene selection.

5. SUPERVISED CLASSIFICATION AND SVM
Supervised classification, also called prediction or discrimination, involves developing algorithms
to priori defined categories. Algorithms are typically developed on a training dataset and then tested
on an independent test dataset to evaluate the accuracy of algorithms. Support vector machines are a
group of related supervised learning methods used for classification and regression. The simplest
type of support vector machines is linear classification which tries to draw a straight line that
separates data with two dimensions. Many linear classifiers (also called hyperplanes) are able to
separate the data. However, only one achieves maximum separation. Vapnik in 1963 proposed a
linear classifier as a original optimal hyper plane algorithm [24]. The replacement of dot product by a
nonlinear kernel function allows the algorithm to fit the maximum-margin hyperplane in the
transformed feature space [23,24]. SVM finds a linear separating hyperplane with the maximal margin
in this higher dimensional space is called the kernel function [24].There are four basic kernels: linear,
polynomial, radial basic function (RBF), and sigmoid [25].

6. WHY SVM FOR CANCER CLASSIFICATION
Gene expression Microarrays are becoming increasingly promising for clinical decision support in
the form of diagnosis and prediction of clinical outcomes of cancer and other complex diseases. In
order to maximize benefits of this technology, researchers are continuously seeking to develop and
apply the most accurate decision support algorithms for the creation of gene expression patient
profiles. Prior research suggests that among well-established and popular techniques for
classification of microarray gene expression data, support vector machine(SVMs) achieve the best
classification performance, significantly out performing K-nearest neighbors, back propagation
neural networks, probabilistic neural networks, weighted voting methods and decision trees.
The reasons for this are
1.SVMs have demonstrated he ability to not only correctly separate entities into appropriate classes,
but also to identify instances whose established classification is not supports by the data.
2.SVM have many mathematical features that make them attractive for gene expression analysis,
including their flexibility in choosing a similarity function, sparseness of solution when dealing
with large data sets, the ability to handle large feature spaces, and the ability to identify outliers.

7.RELATED WORK
In 1999 SVM-based method for directly classifying genes based on microarray data [26] was perhaps
first published. In that features were used along with both the polynomial and Gaussian kernel and
SVM were trained to distinguish between six functional classes, and their performance were
compared with that of four other standard algorithms: Parzen windows, Fisher's linear discriminant,
and C4.5 and MOC1 decision trees. The accuracy of both kernels, in particular the Gaussian kernel,
surpassed those of all four alternative machine learning techniques in terms of overall error rate.
Another early application of SVMs to microarray data was that of tissue classification, one that
remains popular today. Numerous studies released around the same time all achieved similarly
encouraging results: In 1999 [27] provided the first tissue classification algorithm using SVMs,
applied to the problem of differentiating between two types of leukemia. In that feature set was

selected based on the signal-to-noise ratio. The same feature set was used by [26] in 2000 for
classification of ovarian cancer tissues. Both implementations outperformed Naïve Bayes and other
standard machine learning techniques that were typically used for these tasks. The applications of
SVMs to microarray data continue to develop and achieve higher accuracy and robustness.
One particularly intriguing innovation is that in 2008 SVMs are used to classify the pixels
themselves, either into two groups (foreground and background) or three (signal, background and
artifact) [28]. This type of partitioning is typically done using clustering and other unsupervised
machine learning algorithms, but the implementation manages to achieve extremely high accuracy.
The pixels themselves are represented by vectors of eleven distinct features, which measure the
intensity of the pixel, the intensity of its neighbors, and the variation within its neighbors, among
others. The classifiers are trained on already-classified data, and are tested on real new microarrays
and simulated microarrays, along with various types of preprocessing filters. In every case, the
sensitivity of the classifiers exceeds 98%; the accuracy and specificity exceed 99:8%. Recently,
another type of classifier has been gaining attraction in microarray classification, namely random
forests. Random forests are another type of machine learning algorithm which consist of many
randomly-generated (by a bootstrap-like process) decision trees. The output of the classifier for a
given input is the most popular result among all the random trees. In July 2008 [29] released a
comparison of random forests and SVMs for classifying cancer tissue based on microarray data.
According to the authors, random forests, despite their increasing popularity, are still not as accurate
as SVMs for typical microarray classification problems. The metric for the comparison was the area
under the respective ROC curves, as well as the relative classifier information (RCI), an entropybased measure that can be applied to multi-class decision problems. By these measures it is proved
that SVMs out formed random forests on nine of eleven and eleven of eleven tasks, respectively.
SVMs have demonstrated the ability not only correctly separate entities into appropriate classes,
but also to identify mis-labeled data [31].
Another interesting and recent development is due to [30] in 2009, approach is the same standard
binary classification problem as previous researchers, but incorporate network-based information
into the training programs. More specifically, an underlying model of statistically significant
subnetworks is constructed by searching various subnetworks and assigning scores based on each
subnetwork's gene expression level; the algorithm then identifies subnetworks that are capable of
discriminating effectively between categories. Using this information, a penalty term is constructed
which penalizes contradictions between some classification and the corresponding subnetwork
model(s). This penalty term is added to the optimization program itself, rather than incorporated
into the feature space or the kernel function, but the effect is still that the resulting classifiers are
biased towards the underlying subnetwork models. According to the authors, this technique
improves the consistency of the SVM classifier, and also allows for the extraction of higher-level
biological data which is available in other databases and formats. In [32] performance of SVM is
investigated with linear regression and neural network on colon tumor data sets after performing
feature selection. 10 and 50 features were selected by t-statistic feature selection method and
achieved maximum of 85% accuracy on SVM with RBF kernel. In [33] a novel feature selection
method namely recursive feature elimination (RFE) introduced and experiments were done on colon
tumor and leukemia gene expression dataset. With the colon cancer dataset using 4 genes the
method used achieved 98% accuracy.
In [34] Eight data sets used in the experiment and almost in all cases, the accuracy and performance
of classifiers were improved after applying feature selections methods to the datasets. In all cases
SVM-RFE performed very well when it applied with SVM classification methods. In lymphoma
dataset SVM-RFE performed 100% in combination of SVM classification method. In [35] authors

used 10 published microarray datasets, encompassing 6 binary and 4 multiclass classification
Problems and conducted a comprehensive study of both classification methods as well as feature
selection methods for classification of microarray data. All implementations of machine learning
algorithms were taken from the Weka library. Therefore assumed an approximately equal quality of
implementations and differences can be attributed to the methods themselves and not to
implementations. The experiments focused on identifying the best combination of classifier and
feature selection strategy. For this purpose, a selection of common (esp. three SVM variants) and
less common classifiers (such as Voted Perceptron and One Rule) was trained on a large variety of
feature selections, produced by both wrapper and filter strategies. Leave-one-out cross-validation is
used for evaluation and altogether around 220,000 different combinations of classifiers and feature
selections were analyzed. As a general result, it was found that linear SVM to be the best classifier
in the field, closely followed by the quadratic kernel SVM. It is reported that classification using the
SVM method can be improved using a well-chosen size of features together with problemdependent feature selection techniques. For classifiers, the linear and quadratic SVMs show the best
overall performance.
It is Demonstrated that SVM can not only classify new samples, but can also help in the
identification of those which have been wrongly classified by experts [36]. SVMs are unique among
classification methods in this regards.

8. CONCLUSION AND FUTURE DIRECTIONS
This paper reviewed first, the feature selection techniques that have been employed in cancer
classification using gene expression profiles. High dimensionality input and small sample data size
are the main two problems that have been triggers the application of feature selection in microarray
data analysis. Numerous and fruitful efforts have been conducted during the past several years in
the utilization of feature selection to encounter these problems, which mainly can be grouped into
three main approaches; filter, wrapper and embedded approaches. And it is seen that SVM-RFE is
now gaining popularity. Secondly the predominant performance of SVM for Cancer classification is
reviewed. Support Vector Machine (SVM) has recently gained wide popularity among machine
learning community due to its robust mathematical basis and high prediction performance. It has
been successfully applied to the wide variety Cancer classification problems .And it is seen that a
feature selection method called SVM-RFE performed very well when it applied with SVM
classification methods and it is demonstrated that for lymphoma dataset, SVM-RFE performed
100% in combination of SVM classification [34].
A considerable amount of literature has been published on gene selection methods for building
effective classification model. However, a large part of these literatures are statistical analysis, and
their algorithms consider solely on gene expression values to select optimal feature subset.
Although these have shown a promising classification results but there are still some disadvantages
on them. The expression values may not be accurately measured and the complexity of micro array
experiments can causes discrepancy in data obtained. Moreover statistical significance might not
able to directly translate into biological relevance. In recent years, researches have realized that
gene markers identified from microarray drawn from different studies on the same disease across
similar cohorts lack consistency [39,40]. And in the past few years, study on integrative analysis on
micro array data, which is described by [14] as the analysis of high throughput data in the context of
available biological knowledge is gaining popularity.
Recently efforts are directed towards integrative gene selection methods that consider gene
expression data along with additional biological information like Gene Ontology and metabolic
and regulatory pathways (example the MetaCyc and KEGG pathway databases) [41][42][43][44][45].

REFERENCES
[1] R.T.Ng and J.Pei, “Introduction to the special issue on data mining for health Informatics”, SIGKDD
Explore News1,Vol 9 PP1-2 2007
[2] G.Piatetsky-shapiro and P.Tamayo, “Microarray data mining : facing the challenges,” SIGKDD
Explorations Newsletter Vol 5,PP 1-5 December 2003
[3] M.Rocha, R.Mendas, P.Maria, D.Glez-Pena, and F.Fdez-Reverola, “A Platform for the selection of genes
in DNA Microarray data using evolutionary Algorithms,” in Proceedings of 8th Annual Conference on
Genetic and Evolutionary computation, London, England, 2007, pp 415-423.
[4] C.Shang and Q.shen, “Aiding classification of gene expression data with feature selection: A comparative
study”, Vol1, 2006, pp 68-76.
[5] A. Brazma, P. Hingamp, J. Quackenbush, G. Sherlock, P.Spellman, C. Stoeckert, J. Aach, W. Ansorge,
C. A. Ball, H. C. Causton, T. Gaasterland, P. Glenisson, F. C.P.Holstege, I. F. Kim, V. Markowitz, J. C.
Matese, H.Parkinson, A. Robinson, U. Sarkans, S. Schulze-Kremer, J.Stewart, R. Taylor, J. Vilo, and M.
Vingron, “Minimum Information About a Microarray Experiment (MIAME)—Toward Standards for
Microarray Data,” Nature, vol. 29, pp.365-371, 2001.
[6] C. Giallourakis, C. Henson, M. Reich, X. Xie, and V. K. Mootha, “Disease Gene Discovery through
Integrative Genomics,” Annual Review of Genomics and Human Genetics, vol. 6, pp. 381-406, 2005.
[7] Z. Wang, "Neuro-Fuzzy Modeling for Microarray Cancer Gene Expression Data," Oxford University
Computing Laboratory 2005.
[8] S. B. Cho and H. H. Won, "Machine Learning in DNA Microarray Analysis for Cancer Classification,"
presented at Conferences in Research and Practice in Information Technology, Proceedings of the First
Asia-Pacific bioinformatics conference on Bioinformatics, 2003.
[9] H. Hu, J. Li, H. Wang, and G. Daggard, "Combined Gene Selection Methods for Microarray Data
Analysis," in Knowledge-Based Intelligent Information and Engineering Systems, vol. 4251: SpringerVerlag Berlin Heidelberg, 2006, pp. 976–983.
[10] T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J.
R.Downing, M. A. Caligiuri, C. D. Bloomfield, and E. S. Lander, "Molecular Classification of Cancer:
Class
Discovery and Class Prediction by Gene Expression Monitoring," Science, vol. 286, pp. 531-537, 1999.
[11] E. P. Xing, M. I. Jordan, and R. M. Karp, "Feature Selection for High-Dimensional Genomic Microarray
Data," presented at Proc. 18th International Conf. on Machine Learning, 2001.
[12] A. Ben-Dor, L. Bruhn, N. Friedman, I. Nachman, M. e. Schummer, and Z. Yakhini, "Tissue
Classification with Gene Expression Profiles," Journal of Computational Biology, vol. 7, pp. 559-583,
2000.
[13] Y. Barash, E. Dehan, M. Krupsky, W. Franklin, M. Geraci, N. Friedman, and N. Kaminski, "Comparative
analysis of algorithms for signal quantitation from oligonucleotide microarrays," Bioinformatics, vol. 20,
pp. 839–846, 2004.
[14] [14] S. Rogers, R. D. Williams, and C. Campbell, "Class Prediction with Microarray Datasets," in
Bioinformatics Using Computational Intelligence Paradigms, vol. 176: Springer, 2005, pp. 119-141.
[15] B. Y. M. Fung and V. T. Y. Ng, "Classification of Heterogeneous Gene Expression Data," ACM Special
Interest Group on Knowledge Discovery and Data Mining, SIGKDD Explorations., vol. 5, pp. 69 - 78,
2003.
[16] D. Koller and M.Sahami, "Toward Optimal Feature Selection," International Conference of Machine
Learning, pp. 284-292, 1996.

[17] L. Yu and H. Liu, "Redundancy Based Feature Selection for Microarray Data," presented at Proceedings
of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle,
Washington, USA, 2004.
[18] R. Kohavi and G. H. John, "Wrappers for feature subset selection.," Artificial Intelligence, vol. 97, pp.
273-324, 1997.
[19] H. Zhang, T. B. Ho, and S. Kawasaki, "Wrapper Feature Extraction for Time Series Classification Using
Singular Value Decomposition," International Journal of Knowledge and Systems Science, vol. 3, pp. 5360, 2006.
[20] J. J. Liu, G. Cutler, W. Li, Z. Pan, S. Peng, T. Hoey, L. Chen, and X. B. Ling, "Multiclass Cancer
Classification and Biomarker Discovery Using GA-Based Algorithms," Bioinformatics, vol. 21, pp.
2691-2697, 2005.
[21] L. Li, T.A.Darden, C.R.Weingberg, and Levine., "Gene Assessment and Sample Classification for Gene
Expression Data Using a Genetic Algorithm / k-Nearest Neighbor Method," Combinatorial Chemistry &
High Throughput Screening, vol. 4, pp. 727-739, 2001.
[22] I. Guyon, J. Weston, M. D. Stephen Barnhill, and V.Vapnik, "Gene Selection for Cancer Classification
using Support Vector Machines," Machine Learning, vol. 46, pp.389-422, 2002.
[23] Guyon I, Weston J, Barnhill S, Vapnik V: “Gene selection for cancer classification using support vector
machines”. Machine Learning 2001, 46(1–3):389-422.
[24] Vapnik VN: “Statistical Learning Theory: Adaptive and LearningSystems for Signal Processing,
Communications, andControl”. Wiley New York; 1998.
[25] Pirooznia M, Deng Y: “SVM Classifier—a comprehensive java interface for support vector machine
classification of micro array data”. BMC Bioinformatics 2006, 7 Suppl 4:S25.
[26] M. P. S. Brown, W. N. Grundy, D. Lin, N. Cristianini, C. Sugnet, T. S. “Furey, Jr. M. Ares, and D.
Haussler. Knowledge-based analysis of microarray gene expression data using support vector machines”.
Proceedings of the National Academy of Sciences of the United States of America, 97(1):262267, 2000.
[27] S. Ramaswamy, P. Tamayo, R. Rifkin, S. Mukherjee, C. H. Yeang, M. Angelo,C. Ladd, M. Reich, E.
Latulippe, J. P. Mesirov, T. Poggio, W. Gerald, M. Loda,E. S. Lander, and T. R. Golub. “Multiclass
cancer diagnosis using tumor gene expression signatures”. Proceedings of the National Academy of
Sciences of the United States of America, 98(26):1514954, 2001.
[28] Giannakeas, Nikolaos, Karvelis, Petros S., and Fotiadis, Dimitrios I. “A classification-based
segmentation of cDNA microarray images using Support Vector machines. Engineering in Medicine and
Biology Society”, 2008. EMBS 2008. 30th Annual International Conference of the IEEE 20-25 Aug.
2008 Page(s):875 - 878.
[29] Statnikov A, Wang L, Aliferis CF. “A Comprehensive Comparison of Random Forests and Support
Vector Machines for Microarray-Based Cancer Classification”. BMC Bioinformatics, 2008; 9:319.
[30] Zhu, Y., Shen, X., Pan, W. “Network-based support vector machine for classification of microarray
samples”. BMC Bioinformatics 2009, 10(Suppl 1):S21doi:10.1186/1471-2105-10-S1-S21
[31] Support Vector machine classification and validation of cancer tissue samples using microarray
expression data
[32] S. M. Alladi, S. Shantosh, V. Ravi, and U. S. Murthy, "Colon Cancer Prediction with genetic profiles
using intelligent Techniques," Bioinformation, 2008.
[33] I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, "Gene Selection for Cancer Classification using Support
Vector Machines," Machine Learning, vol. 46, pp. 389-422, 2002.

[34] Mehdi Pirooznia, Jack Y Yang, Mary Qu Yang and Youping Deng, “A comparative study of different
machine learning methods on microarray gene expression data” BMC Genomics 2008, 9(Suppl 1):S13
doi:10.1186/1471-2164-9-S1-S13
[35] Stephan Symons and Kay Nieselt_Center for Bioinformatics T¨ubingen , “Data Mining Microarray Data
– Comprehensive Benchmarking of Feature Selection and Classification Methods” Wilhelm-Schickard
Institute for Computer Science, University of T¨ubingen, Sand 14, 72076 T¨ubingen,
[36] Terrence s,Furey,Nello Cristianini,Nigel Duffy,David W.Bennarski,Michel Schummer and David
Haussler, “Support Vector machine classification and validation of cancer tissue samples using micro
array expression data” Oxford University Press 2000.
[37] L. Ying and H. Jiawei, "Cancer classification using gene expression data," Information Systems, vol. 28,
pp. 243-268, 2003.
[38] I. Guyon and A. Elisseeff, "An Introduction to Variable and Feature Selection," Journal of Machine
Learning Research, vol. 3, pp. 1157-1182, 2003.
[39] Ein-Dor L, Kela I, Getz G, Givol D, Domany E:,”Outcome signature genes in breast cancer: is there a
unique set? “Bioinformatics 2005, 21:171-178.
[40] Ein-Dor L, Zuk O, Domany E, “Thousands of samples are needed to generate a robust gene list for
predicting outcome in cancer”. Proc Natl Acad Sci USA 2006, 103:5923-5928.
[41] Olga G. Troyanskaya, “Putting microarrays in a context: Integrated analysis of diverse biological data”,
Henry Stewart Publications 1467-5463. Briefings in. Bioinformatics VOL 6. NO 1. 34–43. MARCH
2005
[42] Franck Rapaport, Andrei Zinovyev, Marie Dutreix, Emmanuel Barillot and Jean-Philippe Vert,
“Classification of microarray data using gene networks”, BMC Bioinformatics 2007, 8:35
doi:10.1186/1471-2105-8-35
[43] Yanni Zhu, Xiaotong Shen and Wei Pan, “Network-based support vector machine for classification of
microarray samples”, The Seventh Asia Pacific Bioinformatics Conference ,BMC Bioinformatics 2009,
10(Suppl 1):S21 doi:10.1186/1471-2105-10-S1-S21
[44] Ong Huey Fang,Norwati Mustapha and Md. Nasir Sulaiman, “Integrating Biological Information for
Feature Selection in Microarray Data Classification” IEEE 2010 Second International Conference on
Computer Engineering and Applications
[45] Yuji Zhang, Jason J. Xuan1, Robert Clarke, Habtom W. Ressom, “Module-Based Biomarker Discovery
in Breast Cancer” 2010 IEEE International Conference on Bioinformatics and Biomedicine,978-1-42448305-1/10©2010 IEEE

ABOUT AUTHORS
[1] Mrs. G.Victo sudha George had completed B.E(CSE) in the year 1993 and M.Tech in the year 2007.
Currently pursuing Ph.d., and the area of research is Bioinformatics. Other areas of interest are
MobileComputing, Data Mining and Artificial Intelligence At present working as Assistant professor in the
Departmentof Computer Science and Engineerng in Dr.M.G.R University ,Chennai,India.
[2] Dr. V.Cyril Raj had completed M.E, and PhD and his areas of interest are Bioinformatics Data
Mining,Grid Computing,Mobile Computing,Robotics etc.He had published a lot of papers in International
and National level journalsand also authored many books.. At present working as Professor and Head of
Computer science and Engg. Dept in Dr.M.G.R University,Chennai,India.

