Classification Of Gene Expression Data Research Articles

Feasible analysis of gene expression –a computational based classification for breast cancer

Computational based classification of gene expression data for analyzing the genetic pattern provides better clinical prediction for breast cancer. Breast cancer is one of the leading cancers among women all over India. This type of cancer leads to malignant tumor developed in the breast. Recent methodologies have undergone many classification methods to analyze the characteristics of gene expression. This paper focuses on computational method such as fuzzy based logistic regression to predict the expression of the gene data. In order to bring accuracy and to solve the inefficiency in feature selection of gene expression data, LASSO Logistic Regression (LLR), a novel methodology is implemented. For computational tractable, Maximum Likelihood Estimation (MLE) is implied with regression model. Diagnosis and prognosis of breast cancer becomes a great challenge in the medical era. This research work explores the mining technology based algorithm to classify the cancer data using fuzzy methodology by evaluating few samples of gene expression data of breast cancer as a training set and the resultant test data are validated to predict the cancer at the earlier stage. For feasible analysis, Expectation Maximization (EM) algorithm is deployed for unknown or missing parameters of gene data expression.

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.

Group K-SVD for the classification of gene expression data

In this paper, we propose a new sparse learning model for the classification of gene expression data, named Group K-Singular Value Decomposition (Group K-SVD). It learns the optimal dictionary and sparse representation from the training data, and then assigns the out-of-sample data to the class with the nearest centroid. Group K-SVD reduces the redundancy of over-complete dictionary by using a group update strategy during the dictionary update stage. To solve the optimization problem, we also develop a Multivariate Orthogonal Matching Pursuit (MOMP) algorithm for the sparse coefficient update stage. In the evaluation of the performance of Group K-SVD model, we implement three Group K-SVD algorithms by incorporating MOMP as well as another two popular optimization algorithms into the sparse coefficient update stage. Sufficient experimental results demonstrate that our Group K-SVD algorithms are both effective and efficient in comparison with the state-of-the-methods on real-world gene expression datasets.

Classifying Gene Expression Data of Cancer Using Multistage Ensemble of Neural Networks

Cancer classification based on the DNA microarray data is a difficult problem because of huge numbers of genes (features) relative to the number of training samples, and out of this huge number of genes most of are irrelevant. Therefore, it is a difficult task to create accurate classifiers for gene expression analysis that handles only a few samples with overwhelming number of noisy genes. Many contributions are already done for cancer classification of DNA microarray using neural network ensemble, which include, weighted averaging, majority voting, simple averaging and ranking. However, each method has some limitations, dependent on the application areas. For the proposed ensemble classification of gene expression data of cancer, first we extract the most relevant genes then apply a new multistage ensembles combination scheme, where the classified copy of results of training samples from first stage neural networks are used as an input features for second stage neural network. In this way we have checked the recognition accuracy for two benchmark gene expression data sets and got the encouraging results.

Classification of stomach cancer gene expression data using CNN algorithm of deep learning

Classification of stomach cancer gene expression data using CNN algorithm of deep learning

A novel microarray gene selection and classification using intelligent dynamic grey wolf optimization

Effective diagnosis of cancer in the medical field is very important to specific treatment. Exact prediction of different cancer types will provide a better treatment and minimization of toxicity in patients. Microarray high dimensionality of gene expression dataand large number of genes against small sample size, noise and repetition in datasets are the main issues which lead to poor classification accuracy. The selection of informative genes and to reduce dimensionality, Gene Selection technique is used in Microarray. In this paper, a novel meta-heurists algorithm based on Grey Wolf Optimization (GWO) and Artificial Intelligence (AI) is combined to design a model for cancer classification. This proposed work consists of two stages. First, a filter method such as Laplacian and Fisher score, are applied to extract the significant subset of features for faster classification and then Intelligent Dynamic Grey Wolf Optimization (IDGWO) is employed to identify the relevant genes. GWO is a swarm-based algorithm selected for gene expression data classification problem, because it makes classification easy about training and testing cancer data. The significant differences between filter methods of datasets are found by using several analyses. The proposed method was applied on five benchmark datasets by considering top 100 ranked genes selected by fisher score in Lymphoma and SRBCT that had a 100% performance using the IDGWO classifier.

Bidirectional compressive sensing for classification of gene expression data

SummaryThe classification of gene expression data is significantly important for medical diagnosis. In recent years, compressive sensing emerges as a popular sparse learning method and has been applied in different areas. It is featured with the sparse representation of data with a few atoms in the dictionary. However, the traditional compressive sensing model only focuses on the relationship among different samples but neglects the relationship among different genes. In order to take into account of the both kinds of correlation, we propose a novel bidirectional compressive sensing model for the classification of gene expression data. Under this model, we develop a novel Bi‐ADMM algorithm with three different variants to solve the optimization problem. The promising experimental results on the real‐world gene expression datasets demonstrate both the effectiveness and efficiency of our proposed approach.

A Deep Neural Network Model using Random Forest to Extract Feature Representation for Gene Expression Data Classification

In predictive model development, gene expression data is associated with the unique challenge that the number of samples (n) is much smaller than the amount of features (p). This “n ≪ p” property has prevented classification of gene expression data from deep learning techniques, which have been proved powerful under “n > p” scenarios in other application fields, such as image classification. Further, the sparsity of effective features with unknown correlation structures in gene expression profiles brings more challenges for classification tasks. To tackle these problems, we propose a newly developed classifier named Forest Deep Neural Network (fDNN), to integrate the deep neural network architecture with a supervised forest feature detector. Using this built-in feature detector, the method is able to learn sparse feature representations and feed the representations into a neural network to mitigate the overfitting problem. Simulation experiments and real data analyses using two RNA-seq expression datasets are conducted to evaluate fDNN’s capability. The method is demonstrated a useful addition to current predictive models with better classification performance and more meaningful selected features compared to ordinary random forests and deep neural networks.

Joint neighborhood entropy-based gene selection method with fisher score for tumor classification

Tumor classification is one of the most vital technologies for cancer diagnosis. Due to the high dimensionality, gene selection (finding a small, closely related gene set to accurately classify tumor) is an important step for improving gene expression data classification performance. Traditional rough set model as a classical attribute reduction method deals with discrete data only. As for the gene expression data containing real-value or noisy data, they are usually employed by a discrete preprocessing, which may result in poor classification accuracy. In this paper, a novel neighborhood rough sets and entropy measure-based gene selection with Fisher score for tumor classification is proposed, which has the ability of dealing with real-value data whilst maintaining the original gene classification information. First, the Fisher score method is employed to eliminate irrelevant genes to significantly reduce computation complexity. Next, some neighborhood entropy-based uncertainty measures are investigated for handling the uncertainty and noisy of gene expression data. Moreover, some of their properties are derived and the relationships among these measures are established. Finally, a joint neighborhood entropy-based gene selection algorithm with the Fisher score is presented to improve the classification performance of gene expression data. The experimental results under an instance and several public gene expression data sets prove that the proposed method is very effective for selecting the most relevant genes with high classification accuracy.

A Gene Expression Data Classification and Selection Method using Hybrid Meta-heuristic technique

The gene expression data selection is an ill-posed problem. The features selection techniques are found to be an efficient way to evaluate the dimensions of huge gene expression data. This feature selection techniques guide the relevant gene selection. In this paper, a hybrid method (MPG) is proposed to get selection of gene expression by using Mutual information way with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). A simulation environment is developed, which reveals the decrease in gene expression data dimensions and also removes the duplication among the classified gene data sets significantly. The proposed approach suitable for gene data set analysis using different classifier techniques and show the higher efficiency and accuracy of proposed data sets as compared to traditional selection mechanisms.

Chronological Pattern Exploration Algorithm for Gene Expression Data Clustering and Classification

In medical data, prediction of a disease in diagnosis and drug determination depends upon the type of the disease. Previous disease classification approaches are clinically based and the ability of the diagnostic process is restricted. In recent days DNA/RNA microarray techniques can monitor more number of gene expression data. Using this ability the medical research people started exploring the various ways of classifying the disease from genetic data. All the methods are promising on producing results. Even though there are some issues which should be handled to improve the accuracy of classification. Classification of the disease depends on data clustering so that, this paper concentrates on clustering the data efficiently and mine for the more accurate pattern using Chronological Pattern Exploration (CPE) as an effective pattern matching approach. This CPE algorithm investigates the data by means of attributes, metadata and the meaning of the data to the search pattern. The simulation of this approach is carried out in MATLAB software to authenticate the efficiency.

Collaborative representation-based classification of microarray gene expression data.

Microarray technology is important to simultaneously express multiple genes over a number of time points. Multiple classifier models, such as sparse representation (SR)-based method, have been developed to classify microarray gene expression data. These methods allocate the gene data points to different clusters. In this paper, we propose a novel collaborative representation (CR)-based classification with regularized least square to classify gene data. First, the CR codes a testing sample as a sparse linear combination of all training samples and then classifies the testing sample by evaluating which class leads to the minimum representation error. This CR-based classification approach is remarkably less complex than traditional classification methods but leads to very competitive classification results. In addition, compressive sensing approach is adopted to project the high-dimensional gene expression dataset to a lower-dimensional space which nearly contains the whole information. This compression without loss is beneficial to reduce the computational load. Experiments to detect subtypes of diseases, such as leukemia and autism spectrum disorders, are performed by analyzing the gene expression. The results show that the proposed CR-based algorithm exhibits significantly higher stability and accuracy than the traditional classifiers, such as support vector machine algorithm.

Classification and Biomarker Genes Selection for Cancer Gene Expression Data Using Random Forest

Microarray and next generation sequencing (NGS) data are the important sources to find helpful molecular patterns. Also, the great number of gene expression data increases the challenge of how to identify the biomarkers associated with cancer. The random forest (RF) is used to effectively analyze the problems of large-p and small-n. Therefore, RF can be used to select and rank the genes for the diagnosis and effective treatment of cancer. The microarray gene expression data of colon, leukemia, and prostate cancers were collected from public databases. Primary preprocessing was done on them using limma package, and then, the RF classification method was implemented on datasets separately in R software. Finally, the selected genes in each of the cancers were evaluated and compared with those of previous experimental studies and their functionalities were assessed in molecular cancer processes. The RF method extracted very small sets of genes while it retained its predictive performance. About colon cancer data set DIEXF, GUCA2A, CA7, and IGHA1 key genes with the accuracy of 87.39 and precision of 85.45 were selected. The SNCA, USP20, and SNRPA1 genes were selected for prostate cancer with the accuracy of 73.33 and precision of 66.67. Also, key genes of leukemia data set were BAG4, ANKHD1-EIF4EBP3, PLXNC1, and PCDH9 genes, and the accuracy and precision were 100 and 95.24, respectively. The current study results showed most of the selected genes involved in the processes and cancerous pathways were previously reported and had an important role in shifting from normal cell to abnormal.

Tumor gene expression data classification via sample expansion-based deep learning.

Since tumor is seriously harmful to human health, effective diagnosis measures are in urgent need for tumor therapy. Early detection of tumor is particularly important for better treatment of patients. A notable issue is how to effectively discriminate tumor samples from normal ones. Many classification methods, such as Support Vector Machines (SVMs), have been proposed for tumor classification. Recently, deep learning has achieved satisfactory performance in the classification task of many areas. However, the application of deep learning is rare in tumor classification due to insufficient training samples of gene expression data. In this paper, a Sample Expansion method is proposed to address the problem. Inspired by the idea of Denoising Autoencoder (DAE), a large number of samples are obtained by randomly cleaning partially corrupted input many times. The expanded samples can not only maintain the merits of corrupted data in DAE but also deal with the problem of insufficient training samples of gene expression data to a certain extent. Since Stacked Autoencoder (SAE) and Convolutional Neural Network (CNN) models show excellent performance in classification task, the applicability of SAE and 1-dimensional CNN (1DCNN) on gene expression data is analyzed. Finally, two deep learning models, Sample Expansion-Based SAE (SESAE) and Sample Expansion-Based 1DCNN (SE1DCNN), are designed to carry out tumor gene expression data classification by using the expanded samples. Experimental studies indicate that SESAE and SE1DCNN are very effective in tumor classification.

A comprehensive simulation study on classification of RNA-Seq data.

RNA sequencing (RNA-Seq) is a powerful technique for the gene-expression profiling of organisms that uses the capabilities of next-generation sequencing technologies. Developing gene-expression-based classification algorithms is an emerging powerful method for diagnosis, disease classification and monitoring at molecular level, as well as providing potential markers of diseases. Most of the statistical methods proposed for the classification of gene-expression data are either based on a continuous scale (eg. microarray data) or require a normal distribution assumption. Hence, these methods cannot be directly applied to RNA-Seq data since they violate both data structure and distributional assumptions. However, it is possible to apply these algorithms with appropriate modifications to RNA-Seq data. One way is to develop count-based classifiers, such as Poisson linear discriminant analysis and negative binomial linear discriminant analysis. Another way is to bring the data closer to microarrays and apply microarray-based classifiers. In this study, we compared several classifiers including PLDA with and without power transformation, NBLDA, single SVM, bagging SVM (bagSVM), classification and regression trees (CART), and random forests (RF). We also examined the effect of several parameters such as overdispersion, sample size, number of genes, number of classes, differential-expression rate, and the transformation method on model performances. A comprehensive simulation study is conducted and the results are compared with the results of two miRNA and two mRNA experimental datasets. The results revealed that increasing the sample size, differential-expression rate and decreasing the dispersion parameter and number of groups lead to an increase in classification accuracy. Similar with differential-expression studies, the classification of RNA-Seq data requires careful attention when handling data overdispersion. We conclude that, as a count-based classifier, the power transformed PLDA and, as a microarray-based classifier, vst or rlog transformed RF and SVM classifiers may be a good choice for classification. An R/BIOCONDUCTOR package, MLSeq, is freely available at https://www.bioconductor.org/packages/release/bioc/html/MLSeq.html.

A hybrid feature selection algorithm for gene expression data classification

In the DNA microarray research field, the increasing sample size and feature dimension of the gene expression data prompt the development of an efficient and robust feature selection algorithm for gene expression data classification. In this study, we propose a hybrid feature selection algorithm that combines the mutual information maximization (MIM) and the adaptive genetic algorithm (AGA). Experimental results show that the proposing MIMAGA-Selection method significantly reduces the dimension of gene expression data and removes the redundancies for classification. The reduced gene expression dataset provides highest classification accuracy compared to conventional feature selection algorithms. We also apply four different classifiers to the reduced dataset to demonstrate the robustness of the proposed MIMAGA-Selection algorithm.

Correlation-based linear discriminant classification for gene expression data.

Microarray gene expression technology provides a systematic approach to patient classification. However, microarray data pose a great computational challenge owing to their large dimensionality, small sample sizes, and potential correlations among genes. A recent study has shown that gene-gene correlations have a positive effect on the accuracy of classification models, in contrast to some previous results. In this study, a recently developed correlation-based classifier, the ensemble of random subspace (RS) Fisher linear discriminants (FLDs), was utilized. The impact of gene-gene correlations on the performance of this classifier and other classifiers was studied using simulated datasets and real datasets. A cross-validation framework was used to evaluate the performance of each classifier using the simulated datasets or real datasets, and misclassification rates (MRs) were computed. Using the simulated data, the average MRs of the correlation-based classifiers decreased as the correlations increased when there were more correlated genes. Using real data, the correlation-based classifiers outperformed the non-correlation-based classifiers, especially when the gene-gene correlations were high. The ensemble RS-FLD classifier is a potential state-of-the-art computational method. The correlation-based ensemble RS-FLD classifier was effective and benefited from gene-gene correlations, particularly when the correlations were high.

Automated Detection of Cancer Associated Genes Using a Combined Fuzzy-Rough-Set-Based F-Information and Water Swirl Algorithm of Human Gene Expression Data.

This study describes a novel approach to reducing the challenges of highly nonlinear multiclass gene expression values for cancer diagnosis. To build a fruitful system for cancer diagnosis, in this study, we introduced two levels of gene selection such as filtering and embedding for selection of potential genes and the most relevant genes associated with cancer, respectively. The filter procedure was implemented by developing a fuzzy rough set (FR)-based method for redefining the criterion function of f-information (FI) to identify the potential genes without discretizing the continuous gene expression values. The embedded procedure is implemented by means of a water swirl algorithm (WSA), which attempts to optimize the rule set and membership function required to classify samples using a fuzzy-rule-based multiclassification system (FRBMS). Two novel update equations are proposed in WSA, which have better exploration and exploitation abilities while designing a self-learning FRBMS. The efficiency of our new approach was evaluated on 13 multicategory and 9 binary datasets of cancer gene expression. Additionally, the performance of the proposed FRFI-WSA method in designing an FRBMS was compared with existing methods for gene selection and optimization such as genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony algorithm (ABC) on all the datasets. In the global cancer map with repeated measurements (GCM_RM) dataset, the FRFI-WSA showed the smallest number of 16 most relevant genes associated with cancer using a minimal number of 26 compact rules with the highest classification accuracy (96.45%). In addition, the statistical validation used in this study revealed that the biological relevance of the most relevant genes associated with cancer and their linguistics detected by the proposed FRFI-WSA approach are better than those in the other methods. The simple interpretable rules with most relevant genes and effectively classified samples suggest that the proposed FRFI-WSA approach is reliable for classification of an individual’s cancer gene expression data with high precision and therefore it could be helpful for clinicians as a clinical decision support system.

A cost-sensitive rotation forest algorithm for gene expression data classification

Existing works show that the rotation forest algorithm has competitive performance in terms of classification accuracy for gene expression data. However, most existing works only focus on the classification accuracy and neglect the classification costs. In this study, we propose a cost-sensitive rotation forest algorithm for gene expression data classification. Three classification costs, namely misclassification cost, test cost and rejection cost, are embedded into the rotation forest algorithm. This extension of the rotation forest algorithm is named as cost-sensitive rotation forest algorithm. Experimental results show that the cost-sensitive rotation forest algorithms effectively reduce the classification cost and make the classification result more reliable.

Fine-grained parallelization of fitness functions in bioinformatics optimization problems: gene selection for cancer classification and biclustering of gene expression data.

BackgroundMetaheuristics are widely used to solve large combinatorial optimization problems in bioinformatics because of the huge set of possible solutions. Two representative problems are gene selection for cancer classification and biclustering of gene expression data. In most cases, these metaheuristics, as well as other non-linear techniques, apply a fitness function to each possible solution with a size-limited population, and that step involves higher latencies than other parts of the algorithms, which is the reason why the execution time of the applications will mainly depend on the execution time of the fitness function. In addition, it is usual to find floating-point arithmetic formulations for the fitness functions. This way, a careful parallelization of these functions using the reconfigurable hardware technology will accelerate the computation, specially if they are applied in parallel to several solutions of the population.ResultsA fine-grained parallelization of two floating-point fitness functions of different complexities and features involved in biclustering of gene expression data and gene selection for cancer classification allowed for obtaining higher speedups and power-reduced computation with regard to usual microprocessors.ConclusionsThe results show better performances using reconfigurable hardware technology instead of usual microprocessors, in computing time and power consumption terms, not only because of the parallelization of the arithmetic operations, but also thanks to the concurrent fitness evaluation for several individuals of the population in the metaheuristic. This is a good basis for building accelerated and low-energy solutions for intensive computing scenarios.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1200-9) contains supplementary material, which is available to authorized users.