Gene Selection Techniques Research Articles

ECAGS: An Enhanced Cancer-Association based Gene Selection Technique for Cancer Patterns Classification and Prediction

ECAGS: An Enhanced Cancer-Association based Gene Selection Technique for Cancer Patterns Classification and Prediction

The Performance Review of mRMR for Gene Selection and Classification of DNA Microarrays

There are two main stages in the classification of DNA microarray data. The first stage is known as gene selection and the second stage is the classification of selected genes. The number of genes produced in high-dimensional microarrays is enormous, and only some of these genes help to identify a particular disease. The selection of relevant or informative genes that provide sufficient information about the condition is therefore essential. Gene selection is vital in reducing the data dimensionality which can ease the workload of the computer and increase the high classification performance. In this paper, we review the recent performance on one of the most popular filter based gene selection technique, maximum relevance minimum redundancy (mRMR). We also discuss several current improvements on the mRMR method.

Two-Stage Hybrid Gene Selection Using Mutual Information and Genetic Algorithm for Cancer Data Classification.

Cancer is a deadly disease which requires a very complex and costly treatment. Microarray data classification plays an important role in cancer treatment. An efficient gene selection technique to select the more promising genes is necessary for cancer classification. Here, we propose a Two-stage MI-GA Gene Selection algorithm for selecting informative genes in cancer data classification. In the first stage, Mutual Information based gene selection is applied which selects only the genes that have high information related to the cancer. The genes which have high mutual information value are given as input to the second stage. The Genetic Algorithm based gene selection is applied in the second stage to identify and select the optimal set of genes required for accurate classification. For classification, Support Vector Machine (SVM) is used. The proposed MI-GA gene selection approach is applied to Colon, Lung and Ovarian cancer datasets and the results show that the proposed gene selection approach results in higher classification accuracy compared to the existing methods.

In silico markers: an evolutionary and statistical approach to select informative genes of human breast cancer subtypes.

Recent advancement in bioinformatics offers the ability to identify informative genes from high dimensional gene expression data. Selection of informative genes from these large datasets has emerged as an issue of major concern among researchers. Gene functionality and regulatory mechanisms can be understood through the analysis of these gene expression data. Here, we present a computational method to identify informative genes for breast cancer subtypes such as Basal, human epidermal growth factor receptor 2 (Her2), luminal A (LumA), and luminal B (LumB). The proposed In Silico Markers method is a wrapper feature selection method based on Least Absolute Shrinkage and Selection Operator (LASSO), Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and Support Vector Machine (SVM) as a classifier. Moreover, the composite measure consisting of relevance, redundancy, and rank score of frequently appeared genes are used to select informative genes. The informative genes are validated by statistical and biologically relevant criteria. For a comparative evaluation of the proposed approach, biological similarity score designed on semantic similarity measure of GO terms are investigated. Further, the proposed technique is evaluated with 7 existing gene selection techniques using two-class annotated breast cancer subtype datasets. The utilization of this method can bring about the discovery of informative genes. Furthermore, under multiple criteria decision-making set-up, informative genes selected by the In Silico Markers are found to be admirable than the compared methods selected genes.

Gene Subset Selection for Leukemia Classification Using Microarray Data

Background: Cancer subtype identification is an active research field which helps in the diagnosis of various cancers with proper treatments. Leukemia is one such cancer with various subtypes. High throughput technologies such as Deoxyribo Nucleic Acid (DNA) microarray are highly active in the field of cancer detection and classification alternatively. Objective: Yet, a precise analysis is important in microarray data applications as microarray experiments provide huge amount of data. Gene selection techniques promote microarray usage in the field of medicine. The objective of gene selection is to select a small subset of genes, which are the most informative in classification. associations while known disease-lncRNA associations are required only. Method: In this study, multi-objective evolutionary algorithm is used for gene subset selection in Leukemia classification. An initial redundant and irrelevant gene removal is followed by multiobjective evolutionary based gene subset selection. Gene subset selection highly influences the perfect classification. Thus, selecting the appropriate algorithm for subset selection is important. Results: The performance of the proposed method is compared against the standard genetic algorithm and evolutionary algorithm. Three Leukemia microarray datasets were used to evaluate the performance of the proposed method. Perfect classification was achieved for all the datasets only with few significant genes using the proposed approach. Conclusion: Thus, it is obvious that the proposed study perfectly classifies Leukemia with only few significant genes.</P>

Graph-Based Hub Gene Selection Technique Using Protein Interaction Information: Application to Sample Classification.

Classification of samples of gene expression profile plays a significant role in prediction and diagnosis of diseases. In the task of sample classification, a robust feature selection algorithm is very much essential to identify the important genes from the high dimensional gene expression data. This paper explores the information of protein-protein interaction with a graph mining technique for finding a proper subset of features (genes), which further takes part in sample classification. Here, our contribution for feature selection is three-fold: first, all the genes are grouped into different clusters based on the integrated information of the gene expression values and their protein interactions using a multi-objective optimization based clustering approach. Second, the confidence scores of the protein interactions are incorporated in a popular graph mining algorithm namely Goldberg algorithm to find out the relevant features. These features are the topologically and functionally significant genes, named as hub genes. Finally, these hub genes are identified varying the degrees of the nodes, and those are utilized for the sample classification task. Different machine learning classifiers are exploited for this purpose, and the classification performance is measured with respect to various performance metrics namely accuracy, sensitivity, specificity, precision, F-measure, and Mathews coefficient correlation. Comparative analysis with respect to two baselines and several existing approaches proves the efficiency of the proposed approach. Furthermore, the robustness of the identified hub-gene modules is endorsed using some strong biological significance analysis.

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.

Gene selection for microarray gene expression classification using Bayesian Lasso quantile regression

Gene selection has been proven to be an effective way to improve the results of many classification methods. However, existing gene selection techniques in binary classification regression are sensitive to outliers of the data, heteroskedasticity or other anomalies of the latent response. In this paper, we propose a new Bayesian hierarchical model to overcome these problems in a relatively straightforward way. In particular, we propose a new Bayesian Lasso method that employs a skewed Laplace distribution for the errors and a scaled mixture of uniform distribution for the regression parameters, together with Bayesian MCMC estimation. Comprehensive comparisons between our proposed gene selection method and other competitor methods are performed experimentally, depending on four benchmark gene expression datasets. The experimental results prove that the proposed method is very effective for selecting the most relevant genes with high classification accuracy.

Gene selection and dynamic neutrosophic cognitive map with bat algorithm (DNCM-BA) for diagnose of rheumatoid arthritis (RAs)

Rheumatoid Arthritis (RA) is an autoimmune inflammatory rheumatic disease that has emotional impact on various body parts and tissue, principally the synovial joints. RA is a complex disease similar to many other autoimmune diseases, in which environmental aspects, genetic variants, as well as arbitrary events cooperate to activate pathological pathways. Choosing the appropriate gene for sample classification is very hard in numerous gene expression analyses in RA, in which authors attempt to find out the least probable set of genes, even now which could attain better predictive performance. On the other hand, the accuracy of classification is not up to the mark. As a result, for identifying RA disease, this research presents a gene selection as well as classification technique. Initially, with the aim of decreasing the time complexity, this disease dataset is preprocessed. Next, so as to decrease the amount of gene, the gene data is chosen from the preprocessed data by means of filter based gene selection techniques: Chi-square (CHI), Information Gain (IG), Consistency Based Subset Evaluation (CS) and Correlation Based Gene Selection (CGS). Thirdly, for the purpose of the classification of RA disease, a Dynamic Neutrosophic Cognitive Map with Bat Algorithm (DNCM-BA) is presented, that is well-suited with the medical routine and it is proposed for supporting gene expression beforehand and accurate diagnosis of RA patients. As a result, RA disease is not permitted from moving to progressive phases and the difficulty of emerging insistent as well as erosive arthritis for RA patients will get reduced. Finally, the outcome confirms that the DNCM-BA technique provides better performance while matched up with FCM–Particle Swarm Optimization (FCM-PSO), Fuzzy C Means (FCMs), Dynamic Firefly Algorithm Fuzzy C Means (DFAFCM) and Dynamic Fuzzy C Mean (DFCM) clustering algorithms in regard to precision, accurateness, recall and F-measure.

Ensemble of rankers for efficient gene signature extraction in smoke exposure classification

BackgroundSystem toxicology aims at understanding the mechanisms used by biological systems to respond to toxicants. Such understanding can be leveraged to assess the risk of chemicals, drugs, and consumer products in living organisms. In system toxicology, machine learning techniques and methodologies are applied to develop prediction models for classification of toxicant exposure of biological systems. Gene expression data (RNA/DNA microarray) are often used to develop such prediction models.ResultsThe outcome of the present work is an experimental methodology to develop prediction models, based on robust gene signatures, for the classification of cigarette smoke exposure and cessation in humans. It is a result of the participation in the recent sbv IMPROVER SysTox Computational Challenge. By merging different gene selection techniques, we obtain robust gene signatures and we investigate prediction capabilities of different off-the-shelf machine learning techniques, such as artificial neural networks, linear models and support vector machines. We also predict six novel genes in our signature, and firmly believe these genes have to be further investigated as biomarkers for tobacco smoking exposure.ConclusionsThe proposed methodology provides gene signatures with top-ranked performances in the prediction of the investigated classification methods, as well as new discoveries in genetic signatures for bio-markers of the smoke exposure of humans.

Statistical approach for selection of biologically informative genes

Selection of informative genes from high dimensional gene expression data has emerged as an important research area in genomics. Many gene selection techniques have been proposed so far are either based on relevancy or redundancy measure. Further, the performance of these techniques has been adjudged through post selection classification accuracy computed through a classifier using the selected genes. This performance metric may be statistically sound but may not be biologically relevant. A statistical approach, i.e. Boot-MRMR, was proposed based on a composite measure of maximum relevance and minimum redundancy, which is both statistically sound and biologically relevant for informative gene selection. For comparative evaluation of the proposed approach, we developed two biological sufficient criteria, i.e. Gene Set Enrichment with QTL (GSEQ) and biological similarity score based on Gene Ontology (GO). Further, a systematic and rigorous evaluation of the proposed technique with 12 existing gene selection techniques was carried out using five gene expression datasets. This evaluation was based on a broad spectrum of statistically sound (e.g. subject classification) and biological relevant (based on QTL and GO) criteria under a multiple criteria decision-making framework. The performance analysis showed that the proposed technique selects informative genes which are more biologically relevant. The proposed technique is also found to be quite competitive with the existing techniques with respect to subject classification and computational time. Our results also showed that under the multiple criteria decision-making setup, the proposed technique is best for informative gene selection over the available alternatives. Based on the proposed approach, an R Package, i.e. BootMRMR has been developed and available at https://cran.r-project.org/web/packages/BootMRMR. This study will provide a practical guide to select statistical techniques for selecting informative genes from high dimensional expression data for breeding and system biology studies.

A hybrid gene selection method for microarray recognition

DNA microarray data is expected to be a great help in the development of efficient diagnosis and tumor classification. However, due to the small number of instances compared to a large number of genes, many of the computational learning methods encounter difficulties to select the low subgroups. In order to select significant genes from the high dimensional data for tumor classification, nowadays, several researchers are exploring microarray data using various gene selection methods. However, there is no agreement between existing gene selection techniques that produce the relevant gene subsets by which it improves the classification accuracy. This motivates us to invent a new hybrid gene selection method which helps to eliminate the misleading genes and classify a disease correctly in less computational time. The proposed method composes of two-stage, in the first stage, EGS method using multi-layer approach and f-score approach is applied to filter the noisy and redundant genes from the dataset. In the second stage, adaptive genetic algorithm (AGA) work as a wrapper to identify significant genes subsets from the reduced datasets produced by EGS that can contribute to detect cancer or tumor. AGA algorithm uses the support vector machine (SVM) and Naïve Bayes (NB) classifier as a fitness function to select the highly discriminating genes and to maximize the classification accuracy. The experimental results show that the proposed framework provides additional support to a significant reduction of cardinality and outperforms the state-of-art gene selection methods regarding accuracy and an optimal number of genes.

An efficient gene selection technique based on Self-organizing Map and Particle Swarm Optimization

An efficient gene selection technique based on Self-organizing Map and Particle Swarm Optimization

Unsupervised gene selection using biological knowledge : application in sample clustering

BackgroundClassification of biological samples of gene expression data is a basic building block in solving several problems in the field of bioinformatics like cancer and other disease diagnosis and making a proper treatment plan. One big challenge in sample classification is handling large dimensional and redundant gene expression data. To reduce the complexity of handling this high dimensional data, gene/feature selection plays a major role.ResultsThe current paper explores the use of biological knowledge acquired from Gene Ontology database in selecting the proper subset of genes which can further participate in clustering of samples. The proposed feature selection technique is unsupervised in nature as it does not utilize any class label information in the process of gene selection. At the end, a multi-objective clustering approach is deployed to cluster the available set of samples in the reduced gene space.ConclusionsReported results show that consideration of biological knowledge in gene selection technique not only reduces the feature space dimensionality in great extent but also improves the accuracy of sample classification. The obtained reduced gene space is validated using strong biological significance tests. In order to prove the supremacy of our proposed gene selection based sample clustering technique, a thorough comparative analysis has also been performed with state-of-the-art techniques.

Bi-stage hierarchical selection of pathway genes for cancer progression using a swarm based computational approach

BackgroundUnderstanding of molecular mechanism, lying beneath the carcinogenic expression, is very essential for early and accurate detection of the disease. It predicts various types of irregularities and results in effective drug selection for the treatment. Pathway information plays an important role in mapping of genotype information to phenotype parameters. It helps to find co-regulated gene groups whose collective expression is strongly associated with the cancer development. MethodIn this paper, we have proposed a bi-stage hierarchical swarm based gene selection technique which combines two methods, proposed in this paper for the first time. First one is a multi-fitness discrete particle swarm optimization (MFDPSO) based feature selection procedure, having multiple fitness functions. This technique uses multi-filtering based gene selection procedure. On top of it, a new blended Laplacian artificial bee colony algorithm (BLABC) is proposed and it is used for automatic clustering of the selected genes obtained from the first procedure. We have performed 10 times 10-fold cross validation and compared our proposed method with various statistical and swarm based gene selection techniques for different popular cancer datasets. ResultExperimental results show that the proposed method as a whole performs significantly well. The MFDPSO based system in combination with BLABC generates a good subset of pathway markers which provides more effective insight into the gene-disease association with high accuracy and reliability.

Robust gene selection methods using weighting schemes for microarray data analysis

BackgroundA common task in microarray data analysis is to identify informative genes that are differentially expressed between two different states. Owing to the high-dimensional nature of microarray data, identification of significant genes has been essential in analyzing the data. However, the performances of many gene selection techniques are highly dependent on the experimental conditions, such as the presence of measurement error or a limited number of sample replicates.ResultsWe have proposed new filter-based gene selection techniques, by applying a simple modification to significance analysis of microarrays (SAM). To prove the effectiveness of the proposed method, we considered a series of synthetic datasets with different noise levels and sample sizes along with two real datasets. The following findings were made. First, our proposed methods outperform conventional methods for all simulation set-ups. In particular, our methods are much better when the given data are noisy and sample size is small. They showed relatively robust performance regardless of noise level and sample size, whereas the performance of SAM became significantly worse as the noise level became high or sample size decreased. When sufficient sample replicates were available, SAM and our methods showed similar performance. Finally, our proposed methods are competitive with traditional methods in classification tasks for microarrays.ConclusionsThe results of simulation study and real data analysis have demonstrated that our proposed methods are effective for detecting significant genes and classification tasks, especially when the given data are noisy or have few sample replicates. By employing weighting schemes, we can obtain robust and reliable results for microarray data analysis.

Statistical Approaches for Gene Selection, Hub Gene Identification and Module Interaction in Gene Co-Expression Network Analysis: An Application to Aluminum Stress in Soybean (Glycine max L.).

Selection of informative genes is an important problem in gene expression studies. The small sample size and the large number of genes in gene expression data make the selection process complex. Further, the selected informative genes may act as a vital input for gene co-expression network analysis. Moreover, the identification of hub genes and module interactions in gene co-expression networks is yet to be fully explored. This paper presents a statistically sound gene selection technique based on support vector machine algorithm for selecting informative genes from high dimensional gene expression data. Also, an attempt has been made to develop a statistical approach for identification of hub genes in the gene co-expression network. Besides, a differential hub gene analysis approach has also been developed to group the identified hub genes into various groups based on their gene connectivity in a case vs. control study. Based on this proposed approach, an R package, i.e., dhga (https://cran.r-project.org/web/packages/dhga) has been developed. The comparative performance of the proposed gene selection technique as well as hub gene identification approach was evaluated on three different crop microarray datasets. The proposed gene selection technique outperformed most of the existing techniques for selecting robust set of informative genes. Based on the proposed hub gene identification approach, a few number of hub genes were identified as compared to the existing approach, which is in accordance with the principle of scale free property of real networks. In this study, some key genes along with their Arabidopsis orthologs has been reported, which can be used for Aluminum toxic stress response engineering in soybean. The functional analysis of various selected key genes revealed the underlying molecular mechanisms of Aluminum toxic stress response in soybean.

Extreme Learning Machine and Fuzzy K-Nearest Neighbour Based Hybrid Gene Selection Technique for Cancer Classification

Extreme Learning Machine and Fuzzy <i>K</i>-Nearest Neighbour Based Hybrid Gene Selection Technique for Cancer Classification

A COMPARATIVE STUDY ON GENE SELECTION METHODS FOR TISSUES CLASSIFICATION ON LARGE SCALE GENE EXPRESSION DATA

Deoxyribonucleic acid (DNA) microarray technology is the recent invention that provided colossal opportunities to measure a large scale of gene expressions simultaneously. However, interpreting large scale of gene expression data remain a challenging issue due to their innate nature of “high dimensional low sample size”. Microarray data mainly involved thousands of genes, n in a very small size sample, p which complicates the data analysis process. For such a reason, feature selection methods also known as gene selection methods have become apparently need to select significant genes that present the maximum discriminative power between cancerous and normal tissues. Feature selection methods can be structured into three basic factions; a) filter methods; b) wrapper methods and c) embedded methods. Among these methods, filter gene selection methods provide easy way to calculate the informative genes and can simplify reduce the large scale microarray datasets. Although filter based gene selection techniques have been commonly used in analyzing microarray dataset, these techniques have been tested separately in different studies. Therefore, this study aims to investigate and compare the effectiveness of these four popular filter gene selection methods namely Signal-to-Noise ratio (SNR), Fisher Criterion (FC), Information Gain (IG) and t-Test in selecting informative genes that can distinguish cancer and normal tissues. In this experiment, common classifiers, Support Vector Machine (SVM) is used to train the selected genes. These gene selection methods are tested on three large scales of gene expression datasets, namely breast cancer dataset, colon dataset, and lung dataset. This study has discovered that IG and SNR are more suitable to be used with SVM. Furthermore, this study has shown SVM performance remained moderately unaffected unless a very small size of genes was selected.

A Hybrid Gene Selection Technique Using Improved Mutual Information and Fisher Score for Cancer Classification Using Microarrays

A Hybrid Gene Selection Technique Using Improved Mutual Information and Fisher Score for Cancer Classification Using Microarrays