Microarray technology provides a way for researchers to measure the expression level of thousands of genes simultaneously in a single experiment. Due to the increasing amount of microarray data, the field of microarray data analysis has become a major topic among researchers. One of the examples of microarray data analysis is classification. Classification is the process of determining the classes for samples. The goal of classification is to identify the differentially expressed genes so that these genes can be used to predict the classes for new samples. In order to perform the tasks of classification of microarray data, classification software is required for effective classification and analysis of large-scale data. This paper reviews numerous classification software applications for gene expression data. In this paper, the reviewed software can be categorized into six supervised classification methods: Support Vector Machine, K-Nearest Neighbour, Neural Network, Linear Discriminant Analysis, Bayesian Classifier, and Random Forest. implementation of software that facilitates and eases the understanding of biological processes. InSrinivasan et al. [3],the most general definition of bioinformatics in addressing biological problems is discussed.Most biomedical researchers are looking for appropriate software which is not only can achieve high prediction accuracy but also includes a user friendly design in order to ease the implementation. Moreover, such softwareisvery useful if the source code is available.In addition, the software should be up-to-date with the related information to make sure that it is competitive with other software.In this paper, the classification software applications for six supervised classification methodsare reviewed. The six supervised classification methods include the Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Neural Network (NN), Bayesian Classifier, Linear Discriminant Analysis (LDA), and Random Forest (RF). Furthermore, the sources of the software and web-based applications are listed as well. Software for Support Vector Machine (SVM) LIBSVMLIBSVM, a library for SVMs, was developed by Chang and Lin [4]. The main purpose of developing this software was to help users implementing SVM. This package supports three main learning tasks: classification, regression, and estimation of probability. For classification, it supports binary and multi-class classification. It also includes various formulations of SVM such as c-classification, v-classification, ∈-regression, and vregression. Other features include support for cross-validation for performance measurement, model selection, and solving of unbalanced data using weighted SVM. It is mainly implemented in C++ and Java but there are many extensions such as R, MATLAB, Python, and Perl that have been developed by Chang and Lin and others. Moreover, it also provides different kernel settings such as linear, polynomial, and radial basis functions. This package is mainly for Windows and Linux. SVMlightSVMlight was developed...

When gene expression data are too large to be processed, they are transformed into a reduced representation set of genes. Transforming large-scale gene expression data into a set of genes is called feature extraction. If the genes extracted are carefully chosen, this gene set can extract the relevant information from the large-scale gene expression data, allowing further analysis by using this reduced representation instead of the full size data. In this paper, we review numerous software applications that can be used for feature extraction. The software reviewed is mainly for Principal Component Analysis (PCA), Independent Component Analysis (ICA), Partial Least Squares (PLS), and Local Linear Embedding (LLE). A summary and sources of the software are provided in the last section for each feature extraction method.