共查询到20条相似文献，搜索用时 156 毫秒
1.
Background
Meta-analysis methods exist for combining multiple microarray datasets. However, there are a wide range of issues associated with microarray meta-analysis and a limited ability to compare the performance of different meta-analysis methods. 相似文献2.
Background
Though microarray experiments are very popular in life science research, managing and analyzing microarray data are still challenging tasks for many biologists. Most microarray programs require users to have sophisticated knowledge of mathematics, statistics and computer skills for usage. With accumulating microarray data deposited in public databases, easy-to-use programs to re-analyze previously published microarray data are in high demand. 相似文献3.
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4.
Hye Young Kim Seo Eun Lee Min Jung Kim Jin Il Han Bo Kyung Kim Yong Sung Lee Young Seek Lee Jin Hyuk Kim 《BMC bioinformatics》2007,8(1):485
Background
The quality of cDNA microarray data is crucial for expanding its application to other research areas, such as the study of gene regulatory networks. Despite the fact that a number of algorithms have been suggested to increase the accuracy of microarray gene expression data, it is necessary to obtain reliable microarray images by improving wet-lab experiments. As the first step of a cDNA microarray experiment, spotting cDNA probes is critical to determining the quality of spot images. 相似文献5.
Background
The ADGE technique is a method designed to magnify the ratios of gene expression before detection. It improves the detection sensitivity to small change of gene expression and requires small amount of starting material. However, the throughput of ADGE is low. We integrated ADGE with DNA microarray (ADGE microarray) and compared it with regular microarray. 相似文献6.
Ki-Yeol Kim Dong Hyuk Ki Ha Jin Jeong Hei-Cheul Jeung Hyun Cheol Chung Sun Young Rha 《BMC bioinformatics》2007,8(1):218
Background
With microarray technology, variability in experimental environments such as RNA sources, microarray production, or the use of different platforms, can cause bias. Such systematic differences present a substantial obstacle to the analysis of microarray data, resulting in inconsistent and unreliable information. Therefore, one of the most pressing challenges in the field of microarray technology is how to integrate results from different microarray experiments or combine data sets prior to the specific analysis. 相似文献7.
Background
The annotations of Affymetrix DNA microarray probe sets with Gene Ontology terms are carefully selected for correctness. This results in very accurate but incomplete annotations which is not always desirable for microarray experiment evaluation. 相似文献8.
Harri T Kiiveri 《BMC bioinformatics》2011,12(1):42
Background
Typical analysis of microarray data ignores the correlation between gene expression values. In this paper we present a model for microarray data which specifically allows for correlation between genes. As a result we combine gene network ideas with linear models and differential expression. 相似文献9.
Wei Shi Ashish Banerjee Matthew E Ritchie Steve Gerondakis Gordon K Smyth 《BMC bioinformatics》2009,10(1):372
Background
Illumina Sentrix-6 Whole-Genome Expression BeadChips are relatively new microarray platforms which have been used in many microarray studies in the past few years. These Chips have a unique design in which each Chip contains six microarrays and each microarray consists of two separate physical strips, posing special challenges for precise between-array normalization of expression values. 相似文献10.
Background
Post-hybridization washing is an essential part of microarray experiments. Both the quality of the experimental washing protocol and adequate consideration of washing in intensity calibration ultimately affect the quality of the expression estimates extracted from the microarray intensities. 相似文献11.
Background
Low-level processing and normalization of microarray data are most important steps in microarray analysis, which have profound impact on downstream analysis. Multiple methods have been suggested to date, but it is not clear which is the best. It is therefore important to further study the different normalization methods in detail and the nature of microarray data in general. 相似文献12.
13.
Background
Non-biological signal (or noise) has been the bane of microarray analysis. Hybridization effects related to probe-sequence composition and DNA dye-probe interactions have been observed in differential methylation hybridization (DMH) microarray experiments as well as other effects inherent to the DMH protocol. 相似文献14.
Background
Complementary DNA (cDNA) microarrays are a well established technology for studying gene expression. A microarray image is obtained by laser scanning a hybridized cDNA microarray, which consists of thousands of spots representing chains of cDNA sequences, arranged in a two-dimensional array. The separation of the spots into distinct cells is widely known as microarray image gridding. 相似文献15.
Gabriela G Loots Patrick SG Chain Shalini Mabery Amy Rasley Emilio Garcia Ivan Ovcharenko 《BMC bioinformatics》2006,7(1):307-8
Background
There are several isolated tools for partial analysis of microarray expression data. To provide an integrative, easy-to-use and automated toolkit for the analysis of Affymetrix microarray expression data we have developed Array2BIO, an application that couples several analytical methods into a single web based utility. 相似文献16.
Background
The analysis of microarray experiments requires accurate and up-to-date functional annotation of the microarray reporters to optimize the interpretation of the biological processes involved. Pathway visualization tools are used to connect gene expression data with existing biological pathways by using specific database identifiers that link reporters with elements in the pathways. 相似文献17.
Background
As the use of microarray technology becomes more prevalent it is not unusual to find several laboratories employing the same microarray technology to identify genes related to the same condition in the same species. Although the experimental specifics are similar, typically a different list of statistically significant genes result from each data analysis. 相似文献18.
Ivan Borozan Limin Chen Bryan Paeper Jenny E Heathcote Aled M Edwards Michael Katze Zhaolei Zhang Ian D McGilvray 《BMC bioinformatics》2008,9(1):305
Background
Gene expression profiling has the potential to unravel molecular mechanisms behind gene regulation and identify gene targets for therapeutic interventions. As microarray technology matures, the number of microarray studies has increased, resulting in many different datasets available for any given disease. The increase in sensitivity and reliability of measurements of gene expression changes can be improved through a systematic integration of different microarray datasets that address the same or similar biological questions. 相似文献19.
Casper J Albers Ritsert C Jansen Jan Kok Oscar P Kuipers Sacha AFT van Hijum 《BMC bioinformatics》2006,7(1):205-14
Background
Simulation of DNA-microarray data serves at least three purposes: (i) optimizing the design of an intended DNA microarray experiment, (ii) comparing existing pre-processing and processing methods for best analysis of a given DNA microarray experiment, (iii) educating students, lab-workers and other researchers by making them aware of the many factors influencing DNA microarray experiments. 相似文献20.