Irrelevant Genes Research Articles

Gene Features Selection for Three-Class Disease Classification via Multiple Orthogonal Partial Least Square Discriminant Analysis and S-Plot Using Microarray Data

MotivationDNA microarray analysis is characterized by obtaining a large number of gene variables from a small number of observations. Cluster analysis is widely used to analyze DNA microarray data to make classification and diagnosis of disease. Because there are so many irrelevant and insignificant genes in a dataset, a feature selection approach must be employed in data analysis. The performance of cluster analysis of this high-throughput data depends on whether the feature selection approach chooses the most relevant genes associated with disease classes.ResultsHere we proposed a new method using multiple Orthogonal Partial Least Squares-Discriminant Analysis (mOPLS-DA) models and S-plots to select the most relevant genes to conduct three-class disease classification and prediction. We tested our method using Golub’s leukemia microarray data. For three classes with subtypes, we proposed hierarchical orthogonal partial least squares-discriminant analysis (OPLS-DA) models and S-plots to select features for two main classes and their subtypes. For three classes in parallel, we employed three OPLS-DA models and S-plots to choose marker genes for each class. The power of feature selection to classify and predict three-class disease was evaluated using cluster analysis. Further, the general performance of our method was tested using four public datasets and compared with those of four other feature selection methods. The results revealed that our method effectively selected the most relevant features for disease classification and prediction, and its performance was better than that of the other methods.

Activation of glyoxylate pathway without the activation of its related gene in succinate-producing engineered Escherichia coli

For the first time, glyoxylate pathway in the biosynthesis of succinate was activated without the genetic manipulations of any gene related with glyoxylate pathway. Furthermore, the inactivation of succinate biosynthesis by-products genes encoding acetate kinase (ackA) and phosphotransacetylase (pta) was proven to be the key factor to activate glyoxylate pathway in the metabolically engineered Escherichia coli under anaerobic conditions. In order to enhance the succinate biosynthesis specifically, the genes (i.e., ldhA, ptsG, ackA-pta, focA-pflB, adhE) that disrupt by-products biosynthesis pathways were combinatorially deleted, while the E. coli malate dehydrogenase (MDH) was overexpression. The highest succinate production of 150.78mM was obtained with YJ003 (ΔldhA, ptsG, ackA-pta), which were 5-folds higher than that obtained with wild type control strain DY329 (25.13mM). For further understand the metabolic response as a result of several genetic manipulations, an anaerobic stoichiometric model that takes into account the glyoxylate pathway have successfully been implemented to estimate the intracellular fluxes in various recombinant E. coli. The fraction to the glyoxylate pathway from OAA in DY329 was 0 and 31% in YJ003, which indicated that even without the absence of the iclR mutation; the glyoxylate pathway was also activated by deleting the by-products biosynthetic genes, and to be responsible for the higher succinate yields. For further strengthen glyoxylate pathway, a two-stage fed-batch fermentation process was developed by using a 600gl−1 glucose feed to achieve a cell growth rate of 0.07h−1 in aerobic fermentation, and using a 750gl−1 glucose feed to maintain the residual glucose concentration around 40gl−1 when its residual level decreased to 10gl−1 in anaerobic fermentation. The best mutant strain YJ003/pTrc99A-mdh produces final succinate concentration of 274mM by fed-batch culture, which was 10-folds higher than that obtained with wild type control strain DY329. This study discovered that glyoxylate pathway could be activated by deleting glyoxylate pathway irrelevant genes (i.e., genes encoding acetate and lactate) and consequently the succinate biosynthesis was effectively improved. This work provides useful information for the modification of metabolic pathway to improve succinate production.

An enhancement of binary particle swarm optimization for gene selection in classifying cancer classes

BackgroundGene expression data could likely be a momentous help in the progress of proficient cancer diagnoses and classification platforms. Lately, many researchers analyze gene expression data using diverse computational intelligence methods, for selecting a small subset of informative genes from the data for cancer classification. Many computational methods face difficulties in selecting small subsets due to the small number of samples compared to the huge number of genes (high-dimension), irrelevant genes, and noisy genes.MethodsWe propose an enhanced binary particle swarm optimization to perform the selection of small subsets of informative genes which is significant for cancer classification. Particle speed, rule, and modified sigmoid function are introduced in this proposed method to increase the probability of the bits in a particle’s position to be zero. The method was empirically applied to a suite of ten well-known benchmark gene expression data sets.ResultsThe performance of the proposed method proved to be superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also requires lower computational time compared to BPSO.

Robust complementary hierarchical clustering for gene expression data analysis by β-divergence

A hierarchical clustering (HC) algorithm is one of the most widely used unsupervised statistical techniques for analyzing microarray gene expression data. When applying the HC algorithm to the gene expression data to cluster individuals, most of the HC algorithms generate clusters based on the highly differentially expressed (DE) genes that have very similar expression patterns. These highly DE genes may sometimes be irrelevant in biological processes. The serious problem is that those irrelevant genes with high expressions potentially drown out the low expressed genes that have important biological functions. To overcome the problem, Nowak and Tibshirani proposed the complementary hierarchical clustering (CHC) (Biostatistics, 9, 467-483, 2008). However, it is not robust against outlying expression and often produces misleading results if there exist some contaminations in the gene expression data. Thus, we propose the robust CHC (RCHC) method to robustify the CHC with respect to outliers by maximizing the β-likelihood function for sequential extraction of a gene-set with proper groups of individuals. Note that the proposed method reduces to the CHC with the tuning parameter β→0. A value of β plays a key role in the performance of the RCHC method, which controls the tradeoff between the robustness and efficiency of the estimators. Using simulation and real gene expression analysis, the RCHC method shows robust properties to gene expression clustering with respect to data contaminations, overcomes the problem of the CHC, and predicts critically important genes from breast cancer data.

A novel multi-stage feature selection method for microarray expression data analysis

With the development of genome research, finding method to classify cancer and detect biomarkers efficiently has become a challenging problem. In this paper, a novel multi-stage method for feature selection is proposed which considers all kinds of genes in the original gene set. The method eliminates the irrelevant, noisy and redundant genes and selects a subset of relevant genes at different stages. The proposed method is examined on microarray datasets of Leukemia, Prostate, Colon, Breast, Nervous and DLBCL by different classifiers and the best accuracies of the method in these datasets are 100%, 98.04%, 100%, 89.74%, 100% and 98.28%, respectively.

Design considerations for massively parallel sequencing studies of common familial cancers

Massively Parallel Sequencing (MPS) allows sequencing of entire exomes and genomes to now be done at reasonable cost, and its utility for identifying genes responsible for rare Mendelian disorders has been demonstrated. However, for a complex disease such as the common cancers, study designs need to accommodate substantial degrees of locus, allelic, and phenotypic heterogeneity, as well as complex relationships between genotype and phenotype. Such considerations include careful selection of samples for sequencing and a well-developed strategy for identifying the few “true” disease susceptibility genes from among the many irrelevant genes that will be found to harbor rare variants. To examine these issues we have performed simulation-based analyses in order to compare several strategies for MPS sequencing in complex disease. Factors examined include genetic architecture, sample size, number and relationship of individuals selected for sequencing, and a variety of filters based on variant type, multiple observations of genes and concordance of genetic variants within pedigrees. A two-stage design was assumed where genes from the MPS analysis of high-risk families are evaluated in a secondary screening phase of a larger set of probands with more modest family histories. Designs were evaluated using a cost function that assumed the cost of sequencing the whole exome was 400 times that of sequencing a single candidate gene. Results indicate that while requiring variants to be identified in multiple pedigrees and/or in multiple individuals in the same pedigree are effective strategies for reducing false positives, there is a danger of over-filtering so that most true susceptibility genes are missed. In most cases, sequencing more than two individuals per pedigree results in reduced power without any benefit in terms of reduced overall cost. Further, our results suggest that although no single strategy is optimal, simulations can provide important guidelines for study design. Examples in familial breast cancer and melanoma will be presented to illustrate these points.

Variational Bayes Procedure for Effective Classification of Tumor Type with Microarray Gene Expression Data

Recently, microarrays that can simultaneously measure the expression levels of thousands of genes have become a valuable tool for classifying tumors. For such classification, where the sample size is usually much smaller than the number of genes, it is essential to construct properly sparse models for accurately predicting tumor types to avoid over-fitting. Bayesian shrinkage estimation is considered a suitable method for providing such sparse models, effectively shrinking estimates of the effects for many irrelevant genes to zero while maintaining those of a small number of relevant genes at significant magnitudes. However, Bayesian analysis usually requires time-consuming computational techniques such as computationally intensive MCMC iterations. This paper describes a computationally effective method of Bayesian shrinkage regression (BSR) incorporating multiple hierarchical structures for constructing a classification model for tumor types using microarray gene expression data. We use a variational approximation method which provides simple approximations of posterior distributions of parameters to reduce computational burden in the Bayesian estimation. This computationally efficient BSR procedure yields a properly sparse model for accurately and rapidly classifying tumor samples. The accuracy of tumor classification is shown to be at least equivalent to that of other methods such as support vector machine and partial least squares using simulated and actual gene expression data sets.

Efficient Classification of Cancer using Support Vector Machines and Modified Extreme Learning Machine based on Analysis of Variance Features

Problem statement: The primary objective is to propose efficient cancer classification techniques which provide reliable and significant classification accuracy. To achieve this primary research goal is to find the smallest set of genes that can ensure high accuracy in classification using supervised machine learning algorithms. The significance of finding the minimum subset is three fold: (a) The computational burden and noise arising from irrelevant genes are much reduced; (b) the cost for cancer testing is reduced significantly as it simplifies the gene expression tests to include only a very small number of genes rather than thousands of genes; (c) it calls for more investigation into the probable biological relationship between these small numbers of genes and cancer development and treatment. Approach: The proposed method involves two steps. In the first step, some important genes are chosen with the help of Analysis of Variance (ANOVA) ranking scheme. In the second step, the classification capability is tested for all simple combinations of those important genes using a better classifier. Results: The proposed method initially uses Support Vector Machine (SVM) classifier. Then Modified Extreme Learning Machine classifier is used for increasing the classification accuracy over SVM. Conclusion: The two datasets are used (Lymphoma and Liver cancer) in the experimental result shows that the proposed method performs the cancer classification with better accuracy when compared to the SVM methods.

A Modified Binary Particle Swarm Optimization for Selecting the Small Subset of Informative Genes From Gene Expression Data

Gene expression data are expected to be of significant help in the development of efficient cancer diagnoses and classification platforms. In order to select a small subset of informative genes from the data for cancer classification, recently, many researchers are analyzing gene expression data using various computational intelligence methods. However, due to the small number of samples compared to the huge number of genes (high dimension), irrelevant genes, and noisy genes, many of the computational methods face difficulties to select the small subset. Thus, we propose an improved (modified) binary particle swarm optimization to select the small subset of informative genes that is relevant for the cancer classification. In this proposed method, we introduce particles' speed for giving the rate at which a particle changes its position, and we propose a rule for updating particle's positions. By performing experiments on ten different gene expression datasets, we have found that the performance of the proposed method is superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also produces lower running times compared to BPSO.

Cancer Classification using Support Vector Machines and Relevance Vector Machine based on Analysis of Variance Features

Problem statement: The objective of this study is, to find the smallest set of genes that can ensure highly accurate classification of cancer from micro array data by using supervised machine learning algorithms. The significance of finding the minimum subset is three fold: The computational burden and noise arising from irrelevant genes are much reduced; the cost for cancer testing is reduced significantly as it simplifies the gene expression tests to include only a very small number of genes rather than thousands of genes; it calls for more investigation into the probable biological relationship between these small numbers of genes and cancer development and treatment. Approach: The proposed method involves two steps. In the first step, some important genes were chosen with the help of Analysis of Variance (ANOVA) ranking scheme. In the second step, the classification capability was tested for all simple combinations of those important genes using a better classifier. Results: The proposed method initially uses Support Vector Machine (SVM) classifier. Relevance Vector Machine (RVM) classifier was used for increasing the classification accuracy over SVM classifier. Conclusion: The experimental result shows that the proposed method performs the cancer classification with better accuracy when compared to the conventional methods.

Design considerations for massively parallel sequencing studies of complex human disease.

Massively Parallel Sequencing (MPS) allows sequencing of entire exomes and genomes to now be done at reasonable cost, and its utility for identifying genes responsible for rare Mendelian disorders has been demonstrated. However, for a complex disease, study designs need to accommodate substantial degrees of locus, allelic, and phenotypic heterogeneity, as well as complex relationships between genotype and phenotype. Such considerations include careful selection of samples for sequencing and a well-developed strategy for identifying the few “true” disease susceptibility genes from among the many irrelevant genes that will be found to harbor rare variants. To examine these issues we have performed simulation-based analyses in order to compare several strategies for MPS sequencing in complex disease. Factors examined include genetic architecture, sample size, number and relationship of individuals selected for sequencing, and a variety of filters based on variant type, multiple observations of genes and concordance of genetic variants within pedigrees. A two-stage design was assumed where genes from the MPS analysis of high-risk families are evaluated in a secondary screening phase of a larger set of probands with more modest family histories. Designs were evaluated using a cost function that assumes the cost of sequencing the whole exome is 400 times that of sequencing a single candidate gene. Results indicate that while requiring variants to be identified in multiple pedigrees and/or in multiple individuals in the same pedigree are effective strategies for reducing false positives, there is a danger of over-filtering so that most true susceptibility genes are missed. In most cases, sequencing more than two individuals per pedigree results in reduced power without any benefit in terms of reduced overall cost. Further, our results suggest that although no single strategy is optimal, simulations can provide important guidelines for study design.

Unimodal transform of variables selected by interval segmentation purity for classification tree modeling of high-dimensional microarray data

As a greedy search algorithm, classification and regression tree (CART) is easily relapsing into overfitting while modeling microarray gene expression data. A straightforward solution is to filter irrelevant genes via identifying significant ones. Considering some significant genes with multi-modal expression patterns exhibiting systematic difference in within-class samples are difficult to be identified by existing methods, a strategy that unimodal transform of variables selected by interval segmentation purity (UTISP) for CART modeling is proposed. First, significant genes exhibiting varied expression patterns can be properly identified by a variable selection method based on interval segmentation purity. Then, unimodal transform is implemented to offer unimodal featured variables for CART modeling via feature extraction. Because significant genes with complex expression patterns can be properly identified and unimodal feature extracted in advance, this developed strategy potentially improves the performance of CART in combating overfitting or underfitting while modeling microarray data. The developed strategy is demonstrated using two microarray data sets. The results reveal that UTISP-based CART provides superior performance to k-nearest neighbors or CARTs coupled with other gene identifying strategies, indicating UTISP-based CART holds great promise for microarray data analysis.

Gene selection and classification using Taguchi chaotic binary particle swarm optimization

The purpose of gene expression analysis is to discriminate between classes of samples, and to predict the relative importance of each gene for sample classification. Microarray data with reference to gene expression profiles have provided some valuable results related to a variety of problems and contributed to advances in clinical medicine. Microarray data characteristically have a high dimension and a small sample size. This makes it difficult for a general classification method to obtain correct data for classification. However, not every gene is potentially relevant for distinguishing the sample class. Thus, in order to analyze gene expression profiles correctly, feature (gene) selection is crucial for the classification process, and an effective gene extraction method is necessary for eliminating irrelevant genes and decreasing the classification error rate.In this paper, correlation-based feature selection (CFS) and the Taguchi chaotic binary particle swarm optimization (TCBPSO) were combined into a hybrid method. The K-nearest neighbor (K-NN) with leave-one-out cross-validation (LOOCV) method served as a classifier for ten gene expression profiles. Experimental results show that this hybrid method effectively simplifies features selection by reducing the number of features needed. The classification error rate obtained by the proposed method had the lowest classification error rate for all of the ten gene expression data set problems tested. For six of the gene expression profile data sets a classification error rate of zero could be reached. The introduced method outperformed five other methods from the literature in terms of classification error rate. It could thus constitute a valuable tool for gene expression analysis in future studies.

TC-VGC: A Tumor Classification System using Variations in Genes’ Correlation

Classification analysis of microarray data is widely used to reveal biological features and to diagnose various diseases, including cancers. Most existing approaches improve the performance of learning models by removing most irrelevant and redundant genes from the data. They select the marker genes which are expressed differently in normal and tumor tissues. These techniques ignore the importance of the complex functional-dependencies between genes. In this paper, we propose a new method for cancer classification which uses distinguished variations of gene–gene correlation in two sample groups. The cancer specific genetic network composed of these gene pairs contains many literature-curated prostate cancer genes, and we were successful in identifying new candidate prostate cancer genes inferred by them. Furthermore, this method achieved a high accuracy with a small number of genes in cancer classification.

Feature gene selection for Chinese hamster classification based on support vector machine

Concerning the gene expression profile of Chinese hamster feature,such as high-dimension and small sample,a method of feature selection for Chinese hamster classification based on Support Vector Machine(SVM) was proposed in this paper.The method used improved FDR gene feature score criterion to remove the genes irrelevant to the classification.A new distance composed by space distance and function distance was proposed as the criterion of comparability to remove redundant genes.A SVM was used as classifier to validate the classification performance of the feature genes selected.The experimental results show that this method effectively removes the irrelevant and redundant genes,and selected the feature genes that meet the needs of least feature genes which classify accurately on Chinese hamster.

Efficient Cancer Classification using Fast Adaptive Neuro-Fuzzy Inference System (FANFIS) based on Statistical Techniques

The increase in number of cancer is detected throughout the world. This leads to the requirement of developing a new technique which can detect the occurrence the cancer. This will help in better diagnosis in order to reduce the cancer patients. This paper aim at finding the smallest set of genes that can ensure highly accurate classification of cancer from micro array data by using supervised machine learning algorithms. The significance of finding the minimum subset is three fold: a) The computational burden and noise arising from irrelevant genes are much reduced; b) the cost for cancer testing is reduced significantly as it simplifies the gene expression tests to include only a very small number of genes rather than thousands of genes; c) it calls for more investigation into the probable biological relationship between these small numbers of genes and cancer development and treatment. The proposed method involves two steps. In the first step, some important genes are chosen with the help of Analysis of Variance (ANOVA) ranking scheme. In the second step, the classification capability is tested for all simple combinations of those important genes using a better classifier. The proposed method uses Fast Adaptive Neuro-Fuzzy Inference System (FANFIS) as a classification model. This classification model uses Modified Levenberg-Marquardt algorithm for learning phase. The experimental results suggest that the proposed method results in better accuracy and also it takes lesser time for classification when compared to the conventional techniques.

Particle swarm optimization with a modified sigmoid function for gene selection from gene expression data

In order to select a small subset of informative genes from gene expression data for cancer classification, many researchers have recently analyzed gene expression data using various computational intelligence methods. However, due to the small number of samples compared with the huge number of genes (high-dimension), irrelevant genes, and noisy genes, many of the computational methods face difficulties in selecting such a small subset. Therefore, we propose an enhancement of binary particle swarm optimization to select the small subset of informative genes that is relevant for classifying cancer samples more accurately. In this method, three approaches have been introduced to increase the probability of the bits in a particle's position being zero. By performing experiments on two gene expression data sets, we have found that the performance of the proposed method is superior to previous related works, including the conventional version of binary particle swarm optimization (BPSO), in terms of classification accuracy and the number of selected genes. The proposed method also produces lower running times compared with BPSO.

Cascade of genetic algorithm and decision tree for cancer classification on gene expression data

Abstract: Cancer classification, through gene expression data analysis, has produced remarkable results, and has indicated that gene expression assays could significantly aid in the development of efficient cancer diagnosis and classification platforms. However, cancer classification, based on DNA array data, remains a difficult problem. The main challenge is the overwhelming number of genes relative to the number of training samples, which implies that there are a large number of irrelevant genes to be dealt with. Another challenge is from the presence of noise inherent in the data set. It makes accurate classification of data more difficult when the sample size is small. We apply genetic algorithms (GAs) with an initial solution provided by t statistics, called t‐GA, for selecting a group of relevant genes from cancer microarray data. The decision‐tree‐based cancer classifier is built on the basis of these selected genes. The performance of this approach is evaluated by comparing it to other gene selection methods using publicly available gene expression data sets. Experimental results indicate that t‐GA has the best performance among the different gene selection methods. The Z‐score figure also shows that some genes are consistently preferentially chosen by t‐GA in each data set.

Informative or Noninformative Calls for Gene Expression: A Latent Variable Approach

The strength and weakness of microarray technology can be attributed to the enormous amount of information it is generating. To fully enhance the benefit of microarray technology for testing differentially expressed genes and classification, there is a need to minimize the amount of irrelevant genes present in microarray data. A major interest is to use probe-level data to call genes informative or noninformative based on the trade-off between the array-to-array variability and the measurement error. Existing works in this direction include filtering likely uninformative sets of hybridization (FLUSH; Calza et al., 2007) and I/NI calls for the exclusion of noninformative genes using FARMS (I/NI calls; Talloen et al., 2007; Hochreiter et al., 2006). In this paper, we propose a linear mixed model as a more flexible method that performs equally good as I/NI calls and outperforms FLUSH. We also introduce other criteria for gene filtering, such as, R2 and intra-cluster correlation. Additionally, we include some objective criteria based on likelihood ratio testing, the Akaike information criteria (AIC; Akaike, 1973) and the Bayesian information criterion (BIC; Schwarz, 1978 ). Based on the HGU-133A Spiked-in data set, it is shown that the linear mixed model approach outperforms FLUSH, a method that filters genes based on a quantile regression. The linear model is equivalent to a factor analysis model when either the factor loadings are set to a constant with the variance of the latent factor equal to one, or if the factor loadings are set to one together with unconstrained variance of the latent factor. Filtering based on conditional variance calls a probe set informative when the intensity of one or more probes is consistent across the arrays, while filtering using R2 or intra-cluster correlation calls a probe set informative only when average intensity of a probe set is consistent across the arrays. Filtering based on likelihood ratio test AIC and BIC are less stringent compared to the other criteria.

Correlation-based Gene Selection and Classification Using Taguchi-BPSO

Microarray data with reference to gene expression profiles have provided some valuable results related to a variety of problems, and contributed to advances in clinical medicine. Microarray data characteristically have a high dimension and small sample size, which makes it difficult for a general classification method to obtain correct data for classification. However, not every gene is potentially relevant for distinguishing the sample class. Thus, in order to analyze gene expression profiles correctly, feature (gene) selection is crucial for the classification process, and an effective gene extraction method is necessary for eliminating irrelevant genes and decreasing the classification error rate. The purpose of gene expression analysis is to discriminate between classes of samples, and to predict the relative importance of each gene for sample classification. In this paper, correlation-based feature selection (CFS) and Taguchi-binary particle swarm optimization (TBPSO) were combined into a hybrid method, and the K-nearest neighbor (K-NN) with leave-one-out cross-validation (LOOCV) method served as a classifier for ten gene expression profiles. Experimental results show that this hybrid method effectively simplifies feature selection by reducing the number of features needed. The classification error rate obtained by the proposed method had the lowest classification error rate for all of the ten gene expression data set problems tested. For six of the gene expression profile data sets a classification error rate of zero could be reached. The introduced method outperformed five other methods from the literature in terms of classification error rate. It could thus constitute a valuable tool for gene expression analysis in future studies.