共查询到20条相似文献,搜索用时 31 毫秒
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Dagmar M Kube Cemile D Savci-Heijink Anne-Françoise Lamblin Farhad Kosari George Vasmatzis John C Cheville Donald P Connelly George G Klee 《BMC molecular biology》2007,8(1):25
Background
To discover prostate cancer biomarkers, we profiled gene expression in benign and malignant cells laser capture microdissected (LCM) from prostate tissues and metastatic prostatic adenocarcinomas. Here we present methods developed, optimized, and validated to obtain high quality gene expression data. 相似文献3.
Pamela A Nieto Paulo C Covarrubias Eugenia Jedlicki David S Holmes Raquel Quatrini 《BMC molecular biology》2009,10(1):63
Background
Normalization is a prerequisite for accurate real time PCR (qPCR) expression analysis and for the validation of microarray profiling data in microbial systems. The choice and use of reference genes that are stably expressed across samples, experimental conditions and designs is a key consideration for the accurate interpretation of gene expression data. 相似文献4.
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Background
A large number of papers have been published on analysis of microarray data with particular emphasis on normalization of data, detection of differentially expressed genes, clustering of genes and regulatory network. On other hand there are only few studies on relation between expression level and composition of nucleotide/protein sequence, using expression data. There is a need to understand why particular genes/proteins express more in particular conditions. In this study, we analyze 3468 genes of Saccharomyces cerevisiae obtained from Holstege et al., (1998) to understand the relationship between expression level and amino acid composition. 相似文献6.
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Background
Gene set enrichment analysis (GSEA) is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. GSEA is especially useful when gene expression changes in a given microarray data set is minimal or moderate. 相似文献8.
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Background
The increasing availability of time-series expression data opens up new possibilities to study functional linkages of genes. Present methods used to infer functional linkages between genes from expression data are mainly based on a point-to-point comparison. Change trends between consecutive time points in time-series data have been so far not well explored. 相似文献10.
Background
Despite the prevalence of horizontal gene transfer (HGT) in bacteria, to this date there were few studies on HGT in the context of gene expression, operons and protein-protein interactions. Using the recently available data set on the E. coli protein-protein interaction network, we sought to explore the impact of HGT on genome structure and protein networks. 相似文献11.
Kim Kultima Birger Scholz Henrik Alm Karl Sköld Marcus Svensson Alan R Crossman Erwan Bezard Per E Andrén Ingrid Lönnstedt 《BMC bioinformatics》2006,7(1):475-27
Background
Two-Dimensional Difference In Gel Electrophoresis (2D-DIGE) is a powerful tool for measuring differences in protein expression between samples or conditions. However, to remove systematic variability within and between gels the data has to be normalized. 相似文献12.
Background
Predictive classification on the base of gene expression profiles appeared recently as an attractive strategy for identifying the biological functions of genes. Gene Ontology (GO) provides a valuable source of knowledge for model training and validation. The increasing collection of microarray data represents a valuable source for generating functional hypotheses of uncharacterized genes. 相似文献13.
Gene set enrichment meta-learning analysis: next- generation sequencing versus microarrays 总被引:1,自引:0,他引:1
Background
Reproducibility of results can have a significant impact on the acceptance of new technologies in gene expression analysis. With the recent introduction of the so-called next-generation sequencing (NGS) technology and established microarrays, one is able to choose between two completely different platforms for gene expression measurements. This study introduces a novel methodology for gene-ranking stability analysis that is applied to the evaluation of gene-ranking reproducibility on NGS and microarray data. 相似文献14.
Background
Large biological data sets, such as expression profiles, benefit from reduction of random noise. Principal component (PC) analysis has been used for this purpose, but it tends to remove small features as well as random noise. 相似文献15.
Bruz Marzolf Eric W Deutsch Patrick Moss David Campbell Michael H Johnson Timothy Galitski 《BMC bioinformatics》2006,7(1):286-7
Background
The biological information in genomic expression data can be understood, and computationally extracted, in the context of systems of interacting molecules. The automation of this information extraction requires high throughput management and analysis of genomic expression data, and integration of these data with other data types. 相似文献16.
Matthew E Ritchie Matthew S Forrest Antigone S Dimas Caroline Daelemans Emmanouil T Dermitzakis Panagiotis Deloukas Simon Tavaré 《BMC bioinformatics》2010,11(1):280
Background
High-throughput measurement of allele-specific expression (ASE) is a relatively new and exciting application area for array-based technologies. In this paper, we explore several data sets which make use of Illumina's GoldenGate BeadArray technology to measure ASE. This platform exploits coding SNPs to obtain relative expression measurements for alleles at approximately 1500 positions in the genome. 相似文献17.
Background
Users of microarray technology typically strive to use universally acceptable data analysis strategies to determine significant expression changes in their experiments. One of the most frequently utilised methods for gene expression data analysis is SAM (significance analysis of microarrays). The impact of selection thresholds, on the output from SAM, may critically alter the conclusion of a study, yet this consideration has not been systematically evaluated in any publication. 相似文献18.
Gerhard G Thallinger Kerstin Baumgartner Martin Pirklbauer Martina Uray Elke Pauritsch Gabor Mehes Charles R Buck Kurt Zatloukal Zlatko Trajanoski 《BMC bioinformatics》2007,8(1):81
Background
With the introduction of tissue microarrays (TMAs) researchers can investigate gene and protein expression in tissues on a high-throughput scale. TMAs generate a wealth of data calling for extended, high level data management. Enhanced data analysis and systematic data management are required for traceability and reproducibility of experiments and provision of results in a timely and reliable fashion. Robust and scalable applications have to be utilized, which allow secure data access, manipulation and evaluation for researchers from different laboratories. 相似文献19.
Background
The paper of Liu, Gaido and Wolfinger on gene expression during the division cycle of HeLa cells using the data of Whitfield et al. are discussed in order to see whether their analysis is related to gene expression during the division cycle. 相似文献20.
Stephen F Madden Susan B Carpenter Ian B Jeffery Harry Björkbacka Katherine A Fitzgerald Luke A O'Neill Desmond G Higgins 《BMC bioinformatics》2010,11(1):257