Classification Of Microarray Gene Expression Data Research Articles

Microarray Gene Expression Data Classification via Wilcoxon Sign Rank Sum and Novel Grey Wolf Optimized Ensemble Learning Models.

Cancer is a deadly disease that affects the lives of people all over the world. Finding a few genes relevant to a single cancer disease can lead to effective treatments. The difficulty with microarray datasets is their high dimensionality; they have a large number of features in comparison to the small number of samples in these datasets. Additionally, microarray data typically exhibit significant asymmetry in dimensionality as well as high levels of redundancy and noise. It is widely held that the majority of genes lack informative value about the classes under study. Recent research has attempted to reduce this high dimensionality by employing various feature selection techniques. This paper presents new ensemble feature selection techniques via the Wilcoxon Sign Rank Sum test (WCSRS) and the Fisher's test (F-test). In the first phase of the experiment, data preprocessing was performed; subsequently, feature selection was performed via the WCSRS and F-test in such a way that the (probability values) p-values of the WCRSR and F-test were adopted for cancerous gene identification. The extracted gene set was used to classify cancer patients using ensemble learning models (ELM), random forest (RF), extreme gradient boosting (Xgboost), cat boost, and Adaboost. To boost the performance of the ELM, we optimized the parameters of all the ELMs using the Grey Wolf optimizer (GWO). The experimental analysis was performed on colon cancer, which included 2000 genes from 62 patients (40 malignant and 22 benign). Using a WCSRS test for feature selection, the optimized Xgboost demonstrated 100% accuracy. The optimized cat boost, on the other hand, demonstrated 100% accuracy using the F-test for feature selection. This represents a 15% improvement over previously reported values in the literature.

A hybrid of Information gain and a Coati Optimization Algorithm for gene selection in microarray gene expression data classification.

A hybrid of Information gain and a Coati Optimization Algorithm for gene selection in microarray gene expression data classification.

Metalearning approach for leukemia informative genes prioritization.

The discovery of diagnostic or prognostic biomarkers is fundamental to optimize therapeutics for patients. By enhancing the interpretability of the prediction model, this work is aimed to optimize Leukemia diagnosis while retaining a high-performance evaluation in the identification of informative genes. For this purpose, we used an optimal parameterization of Kernel Logistic Regression method on Leukemia microarray gene expression data classification, applying metalearners to select attributes, reducing the data dimensionality before passing it to the classifier. Pearson correlation and chi-squared statistic were the attribute evaluators applied on metalearners, having information gain as single-attribute evaluator. The implemented models relied on 10-fold cross-validation. The metalearners approach identified 12 common genes, with highest average merit of 0.999. The practical work was developed using the public datamining software WEKA.

DNA Microarray Gene Expression Data Classification Using SVM, MLP, and RF with Feature Selection Methods Relief and LASSO

DNA microarray technology is a novel method to monitor expression levels of large number of genes simultaneously. These gene expressions can be and is being used to detect various forms of diseases. Using multiple microarray datasets, this paper cross compares two different methods for classification and feature selection. Since individual gene count in microarray datas are too many, most informative genes should be selected and used. For this selection, we have tried Relief and LASSO feature selection methods. After selecting informative genes from microarray data, classification is performed with Support Vector Machines (SVM) and Multilayer Perceptron Networks (MLP) which both are widely used in multiple classification tasks. The overall accuracy with LASSO and SVM outperforms most of the approaches proposed.

A hybrid heuristic dimensionality reduction technique for microarray gene expression data classification: a blending of GA, PSO and ACO

The micro-array gene expression data are mostly utilised for biological applications for classification of tumours in genes. Majority of recent work are concentrated to reduce the dimension of micro-array gene expression data. The modern dimension reduction approaches have followed statistical techniques and transformation techniques like principal component analysis (PCA). This paper deals with a hybrid heuristic dimensionality reduction technique for microarray gene expression data classification using biologically inspired heuristic algorithms. The proposed hybrid heuristic technique has combined genetic algorithm (GA), particle swarm optimisation (PSO) and ant colony optimisation (ACO). The dimensionality reduced data using the proposed technique correlates well with the classifier with less classification error after training. The test case investigation is performed with acute lymphoblastic leukaemia (ALL) and acute myeloid leukaemia (AML) dataset and the classification accuracy of the proposed technique are compared with conventional technique.

Classification of Micro Array Gene Expression Proposed using Statistical Approaches

Classification analysis of microarray gene expression data has been performed widely to find out the biological features and to differentiate intimately related cell types that usually appear in the diagnosis of cancer. Many algorithms and techniques have been developed for the microarray gene classification process. These developed techniques accomplish microarray gene classification process with the aid of three basic phases namely, dimensionality reduction, feature selection and gene classification. In our previous work, microarray gene classification by statistical analysis approach with Fuzzy Inference System (FIS) was proposed for precise classification of genes to their corresponding gene types. Among various dimensionality reduction techniques, the paper proposed prescribed statistical procedures to efficiently perform the classification process. To further substantiate and to analyze the performance, we conduct a comparative study in this work. The comparative study considers two popular dimensionality reduction techniques called Principle Component Analysis (PCA) and Multi-linear Principle Component Analysis (MPCA). The dimensionality reduction techniques replace the proposed statistical approach and perform microarray gene expression data classification. Based on the obtained results, we conduct the performance study over the combination of statistical approach with FIS, LPP with FIS and MPCA with FIS. The study results that the statistical approach with FIS outperforms the classification performance when compared to the other methods.

Microarray gene-expression data classification using less gene expressions by combining feature selection methods and classifiers

Microarray Data, often characterised by high-dimensions and small samples, is used for cancer classification problems that classify the given (tissue) samples as deceased or healthy on the basis of analysis of gene expression profile. The goal of feature selection is to search the most relevant features from thousands of related features of a particular problem domain. The focus of this study is a method that relaxes the maximum accuracy criterion for feature selection and selects the combination of feature selection method and classifier that using small subset of features obtains accuracy not statistically indicatively different than the maximum accuracy. By selecting the classifier employing small number of features along with a good accuracy, the risk of over fitting (bias) is reduced. This has been corroborated empirically using some common attribute selection methods (ReliefF, SVM-RFE, FCBF, and Gain Ratio) and classifiers (3 Nearest Neighbour, Naive Bayes and SVM) applied to 6 different microarray cancer data sets. We use hypothesis testing to compare several configurations and select particular configurations that perform well with small genes on these data sets.

On the classification of microarray gene-expression data

We consider the classification of microarray gene-expression data. First, attention is given to the supervised case, where the tissue samples are classified with respect to a number of predefined classes and the intent is to assign a new unclassified tissue to one of these classes. The problems of forming a classifier and estimating its error rate are addressed in the context of there being a relatively small number of observations (tissue samples) compared to the number of variables (that is, the genes, which can number in the tens of thousands). We then proceed to the unsupervised case and consider the clustering of the tissue samples and also the clustering of the gene profiles. Both problems can be viewed as being non-standard ones in statistics and we address some of the key issues involved. The focus is on the use of mixture models to effect the clustering for both problems.

Classification of gene-expression data: The manifold-based metric learning way

Classification of microarray gene-expression data can potentially help medical diagnosis, and becomes an important topic in bioinformatics. However, microarray data sets are usually of small sample size relative to an overwhelming number of genes. This makes the classification problem fairly challenging. Instance-based learning (IBL) algorithms, such as nearest neighbor ( k-NN), are usually the baseline algorithm due to their simplicity. However, practices show that k-NN performs not very well in this field. This paper introduces manifold-based metric learning to improve the performance of IBL methods. A novel metric learning algorithm is proposed by utilizing both local manifold structural information and local discriminant information. In addition, a random subspace extension is also presented. We apply the proposed algorithm to the gene-classification problem in three ways: one in the original feature space, another in the reduced feature space, and the third via the random subspace extension. Statistical evaluation shows that the proposed algorithm can achieve promising results, and gain significant performance improvement over traditional IBL algorithms.

Comparison of Support Vector Machines to Other Classifiers Using Gene Expression Data

ABSTRACT Support vector machines (SVMs) was shown to outperform Fisher's linear discriminant analysis and two classification trees (C4.5 and MOC1) in binary classification of microarray gene expression data (MGED) (Brown et al., 2000; Furey et al. 2000). However, multiclass classification is more commonly encountered in identifying tumor subtypes using MGED. Using MGED, Dudoit et al. (2002) showed that diagonal linear discriminant analysis (DLDA) outperformed other linear and quadratic discriminants, nearest neighbor, and classification trees with univariate splits. It is of interest, therefore, to compare performance of SVMs to DLDA and the latest two classification trees with linear splits, which performered better than trees with univariate splits, in multiclass classification of MGED. Furthermore, the performance of SVMs with different types of kernels were studied by three types of multiclass MGED. Finally, we investigate how irrelevant and correlated variables (features) influence the performance of the three classifiers. Some suggestions are made for multiclass classification of MGED.