An adaptive classification model for microarray analysis using big data

The real-time system of the JT-60 has a cascade structure with the major and the minor control loops. The multi-variable feedback control is adopted in the major control loop in order to adjust non-linear effects which will appear in the high-/beta/region. By applying the concept of 'phase shift control' to the control program of the major control loop, a flexible tokamak discharge control is expected to be performed with taking account of machine protection.

A proposal for direct-ordering gene expression data by self-organising maps

We present Genome3DExploerer, an immersive visual mining tool to explore microarray data with other sources. The paper addresses the problem of exploring huge genome expression data, allowing biologists to group gene expression data in immersive environment. This could be a solution to explore augmented microarray data with other gene information sources. A microarray data set represents thousands of genes' expression levels in various experimental conditions. Genome3DExploerer handles co-expression patterns, namely clusters of genes with correlated expression profiles. Classical clustering methods offer biologists to group genes in distinct groups. Nevertheless, these methods can not deal with genes similar to several ones or those shared by distinct clusters. Moreover, the visualization techniques associated with these methods are not well adapted in order to explore huge amount of microarray data. We present in this work a new approach, based on dynamic graph immersive visualization, which offers a solution to process these microarray data characteristics.

The genome is now accessible to the endoscopist

DNA microarrays are used to measure the expression levels of thousands of genes simultaneously. In a time series experiment, the gene expressions are measured as a function of time. We present an application for integrated visualization of genome expression and network dynamics in both regulatory networks and metabolic pathways. Integration of these two levels of cellular processes is necessary, since it provides the link between the measurements at the transcriptional level (gene expression levels approximated from microarray data) and the phenotype (the observable characteristics of an organism) at the functional and behavioral level. The integration requires visualization approaches besides traditional clustering and statistical analysis methods. Our application can (i) visualize the data from time series experiments in the context of a regulatory network and KEGG metabolic pathways; (ii) identify and visualize active regulatory subnetworks from the gene expression data; (iii) perform a statistical test to identify and subsequently visualize pathways that are affected by differentially expressed genes. We present a case study, which demonstrates that our approach and application both facilitates and speeds up data analysis tremendously in comparison to a more traditional approach that involves many manual, laborious, and error‐prone steps.

Sequence databases and microarrays as tools for identifying prostate cancer biomarkers

E4F1, a Novel Estrogen-Responsive Gene in Possible Atheroprotection, Revealed by Microarray Analysis

Cellular water-deficit stress triggers many changes in gene expression which can be used to define the response of a plant to an environmental condition. Microarray technology permits the study of expression patterns of thousands of genes simultaneously, permitting a comprehensive understanding of the types and quantities of RNAs that are present in a cell in response to water-deficit stress. The expression of specific genes was compared in three different experiments designed to understand changes in gene expression in response to water-deficit stress. Surprisingly, there was a relatively small set of genes that were commonly induced or repressed. There were 27 genes commonly induced and three commonly repressed; 1.4% and 0.2% of the genes analysed in common to all three experiments. The induced genes fell into six different functional categories: metabolism, transport, signalling, transcription, hydrophilic proteins, and unknown. The three commonly repressed genes indicated that repression of gene expression supported a frequently observed response to water-deficit stress, decreased growth. A more detailed analysis of genes involved in cell wall metabolism, indicated that there was a global decrease in expression of genes that promote cell expansion.

DNA microarray analysis in down syndrome

BackgroundClassification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that are important for distinguishing the different sample classes being compared, poses a challenging problem in high dimensional data analysis. We describe a new procedure for selecting significant genes as recursive cluster elimination (RCE) rather than recursive feature elimination (RFE). We have tested this algorithm on six datasets and compared its performance with that of two related classification procedures with RFE.ResultsWe have developed a novel method for selecting significant genes in comparative gene expression studies. This method, which we refer to as SVM-RCE, combines K-means, a clustering method, to identify correlated gene clusters, and Support Vector Machines (SVMs), a supervised machine learning classification method, to identify and score (rank) those gene clusters for the purpose of classification. K-means is used initially to group genes into clusters. Recursive cluster elimination (RCE) is then applied to iteratively remove those clusters of genes that contribute the least to the classification performance. SVM-RCE identifies the clusters of correlated genes that are most significantly differentially expressed between the sample classes. Utilization of gene clusters, rather than individual genes, enhances the supervised classification accuracy of the same data as compared to the accuracy when either SVM or Penalized Discriminant Analysis (PDA) with recursive feature elimination (SVM-RFE and PDA-RFE) are used to remove genes based on their individual discriminant weights.ConclusionSVM-RCE provides improved classification accuracy with complex microarray data sets when it is compared to the classification accuracy of the same datasets using either SVM-RFE or PDA-RFE. SVM-RCE identifies clusters of correlated genes that when considered together provide greater insight into the structure of the microarray data. Clustering genes for classification appears to result in some concomitant clustering of samples into subgroups.Our present implementation of SVM-RCE groups genes using the correlation metric. The success of the SVM-RCE method in classification suggests that gene interaction networks or other biologically relevant metrics that group genes based on functional parameters might also be useful.

Microarrays are capable of determining the expression levels of thousands of genes simultaneously. One important application of gene expression data is classification of samples into categories. In combination with classification methods, this technology can be useful to support clinical management decisions for individual patients, e.g. in oncology. Standard statistic methodologies in classification or prediction do not work well when the number of variables p (genes) far too exceeds the number of samples n. So, modification of existing statistical methodologies or development of new methodologies is needed for the analysis of microarray data. This paper proposes a new method for tumor classification using gene expression data. In this method, we first employ independent component analysis to model the gene expression data, then apply optimal scoring algorithm to classify them. Further speaking, this approach can first make full use of the high-order statistical information contained in the gene expression data. Second, this approach also employs regularized regression models to handle the situation of large numbers of correlated predictor variables. Finally, the predictive models are developed for classifying tumors based on the entire gene expression profile. To show the validity of the proposed method, we apply it to classify four DNA microarray datasets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible. Matlab scripts are available on request.

Microarray technology makes it easier for scientists to rapidly measure thousands of gene’s expression levels. By analyzing these data, we can find the altered genes, thereby facilitating easy diagnosis and classification of the genetic-related diseases. However, predicting and identifying cancer types is a great challenge in the medical field. Gene expression microarray contains information that can help in this regard, but microarray data have high dimensionality problem which means a large number of genes or features and a small number of samples, also there are redundant and irrelevant features that increase the challenge of microarray analysis. This study reviewed recent studies about methods, algorithms, and limitations of feature selection for microarray gene expression classification. This study compared and focused on four aspects for each related study: datasets, feature selection methods, classifiers, and accuracy results. Feature selection methods are considered as a pre-processing step which plays a vital role in the effectiveness of a classification. This paper showed that applying filter methods such as t-Test, Pearson’s Correlation Coefficient (PCC), and Bhattacharyya distance eliminate irrelevant features that help to increase classification performance and accuracy. Therefore, applying wrapper or embedded methods such as Genetic Algorithm (GA) without applying filter methods in advance could affect the effectiveness of a classification negatively.

In order to understand even the simplest cellular processes, we need to integrate proteomic, gene expression and other biomolecular data. To date, most computational approaches aimed at integrating proteomics and gene expression data use direct gene/protein correlation measures. However, due to post-transcriptional and translational regulations, the correspondence between the expression of a gene and its protein is complicated. We apply a multivariate statistical method, co-inertia analysis (CIA), to visualise gene and proteomic expression data stemming from the same biological samples. Principal components analysis or correspondence analysis can be used for data exploration on single datasets. CIA is then used to explore the relationships between two or more datasets. We further explore the data by projecting gene ontology (GO) information onto these plots to describe the cellular processes in action. We apply these techniques to gene expression and protein abundance data from studies of the human malarial parasite life cycle and the NCI-60 cancer cell lines. In each case, we visualise gene expression, protein abundance and GO classes in the same low dimensional projections and identify GO classes that are likely to be of biological importance.

<p>Figure S1. Experimental Figure Scheme. Figure S2. ChIP-Seq quality assessment for different samples and histone marks. Figure S3. Comparable ChIP-Seq and ChIP-qRT-PCR detection of histone enrichment for different histone marks near three reference genes. Figure S4. Comparable expression of reference genes. Figure S5. H3K9ac histone mark enrichment distribution near transcription start sites. Figure S6. H3K27ac histone mark enrichment distribution near transcription start sites. Figure S7. H3K9me3 histone mark enrichment distribution near transcription start sites. Figure S8. Correlation between tissue specific H3K9ac histone peak enrichment and expression of nearby genes. Figure S9. Correlation between tissue specific H3K9me3 histone peak enrichment and expression of nearby genes. Figure S10. Correlation between tissue specific H3K4me3 histone peak enrichment and expression of nearby genes for six study samples and two PDX-parental tissues. Figure S11. Correlation between tissue specific H3K27ac histone peak enrichment and expression of nearby genes for six study samples and two PDX-parental tissues. Figure S12. Correlation between tissue specific H3K9ac histone peak enrichment and expression of nearby genes for six study samples and two PDX-parental tissues. Figure S13. Correlation between tissue specific H3K9me3 histone peak enrichment and expression of nearby genes for six study samples and two PDX-parental tissues. Figure S14. Scheme for integration of ChIP-Seq Specific Histone Peaks with Expression Variation Analysis (EVA). Figure S15. Correlation of H3K27ac-enriched with genes differentially regulated in mesenchymal and classical HPV+ HNSCC subtypes.</p>

Immunomodulatory drugs lenalidomide and pomalidomide inhibit multiple myeloma-induced osteoclast formation and the RANKL/OPG ratio in the myeloma microenvironment targeting the expression of adhesion molecules