ライフサイエンスコーパス: microarray experiment

1 sis was conducted to assess the quality of a microarray experiment.

2 platforms for assessment of performance of a microarray experiment.

3 nd technical variability is intrinsic in any microarray experiment.

4 d the gene expression difference seen in the microarray experiment.

5 f genetic expression levels measured in each microarray experiment.

6 d from a high-density oligonucleotide tiling microarray experiment.

7 of gene expression level obtained from each microarray experiment.

8 ressed genes is a fundamental objective of a microarray experiment.

9 ssing genes between two patient groups using microarray experiment.

10 and aids the inferring of conclusions from a microarray experiment.

11 ations and a two-channel dye-swapped spotted microarray experiment.

12 eous error variability in an oligonucleotide microarray experiment.

13 hen testing for differential expression in a microarray experiment.

14 description of the variability sources in a microarray experiment.

15 differentially expressed genes identified by microarray experiment.

16 hnical variation between samples in a single microarray experiment.

17 erturbation responses over a large number of microarray experiments.

18 ul for improving the reliability of microRNA microarray experiments.

19 is Not Cyber-T) that can analyze Affymetrix microarray experiments.

20 e of P. furiosus to S(0), as revealed by DNA microarray experiments.

21 roarray Experiment (MIAME) specification for microarray experiments.

22 is illustrated by a combined analysis of 20 microarray experiments.

23 be very timeconsuming, especially for large microarray experiments.

24 or differential expression analyses of small microarray experiments.

25 es of hundreds of TFs in any of thousands of microarray experiments.

26 ameters are often overlooked when optimizing microarray experiments.

27 the differential analysis of gene expression microarray experiments.

28 onal artifacts) to detect these artifacts in microarray experiments.

29 er in these validation experiments as in the microarray experiments.

30 the identification of enriched gene sets for microarray experiments.

31 data from enzyme-linked immunosorbent assay microarray experiments.

32 re to call single-feature polymorphisms from microarray experiments.

33 C to 8 or 15 degrees C based on time series microarray experiments.

34 hat frequently arise in the meta-analysis of microarray experiments.

35 lization of co-expressed genes identified by microarray experiments.

36 thousands of motifs derived from hundreds of microarray experiments.

37 e-level analysis of cDNA and oligonucleotide microarray experiments.

38 mRNA is then amplified and used as probe in microarray experiments.

39 g greatly improved reliability to individual microarray experiments.

40 alyzing the large datasets arising from cDNA microarray experiments.

41 rovides insights useful for understanding of microarray experiments.

42 ng twofold or less signal changes across two microarray experiments.

43 cally available siRNA data from more than 50 microarray experiments.

44 er gene expression profiles assessed by cDNA microarray experiments.

45 affects both the analysis and the results of microarray experiments.

46 program for the significance analysis of DNA microarray experiments.

47 ies, but its applicability is not limited to microarray experiments.

48 een systematically studied in the context of microarray experiments.

49 plant material using the results of over 320 microarray experiments.

50 as a proxy for gene expression in dual-label microarray experiments.

51 er animal and sections per array in planning microarray experiments.

52 sion of measurements obtained from replicate microarray experiments.

53 ent from that of fluorescence intensities in microarray experiments.

54 mula, can be used to assist in the design of microarray experiments.

55 a from both the cDNA and Affymetrix GeneChip microarray experiments.

56 researchers prioritize follow up studies to microarray experiments.

57 lyse a total number of 156 172 spots from 12 microarray experiments.

58 variability stem from experimental errors in microarray experiments.

59 expressed genes is one of the major goals of microarray experiments.

60 usually larger than the number of available microarray experiments.

61 the predictions that were made based on the microarray experiments.

62 -dependent systematic effects in two-channel microarray experiments.

63 outlined regarding the details of conducting microarray experiments.

64 ious comparative genomic studies and ongoing microarray experiments.

65 r interpretation of results from E. coli DNA microarray experiments.

66 sing genome-wide comparative analysis of DNA microarray experiments.

67 n sets of genes derived from sources such as microarray experiments.

68 r-specific gene expression to validate human microarray experiments.

69 iodically expressed (PE) genes in cell cycle microarray experiments.

70 obstacle to obtaining high quality data from microarray experiments.

71 er randomization is essential for successful microarray experiments.

72 ta, and motifs from in vitro protein binding microarray experiments.

73 of curated, publicly available breast cancer microarray experiments.

74 kemia), consistent with findings in previous microarray experiments.

75 of the gene lists generated from these three microarray experiments.

76 d the technological processes underlying the microarray experiments.

77 e expression analyses were performed through microarray experiments.

78 of) differentially expressed transcripts in microarray experiments.

79 ding differentially expressed genes in small microarray experiments.

80 cally related sets, or of results from other microarray experiments.

81 s to find optimal loop designs for two-color microarray experiments.

82 s and p values) from various cancer-specific microarray experiments.

83 Advancing beyond the standard red/green microarray experiment, a panel of eight reporters were l

84 i.e. to provide the semantics to describe a microarray experiment according to the concepts specifie

85 Microarray experiments allow the interrogation of tens o

86 izing a temporal, photic and pharmacological microarray experiment allowed us to identify novel genes

87 Microarray experiments also confirmed that the pattern o

88 s that are close on the microtiter plates in microarray experiments also tend to have higher correlat

89 the minimum information needed to describe a microarray experiment and the Microarray Gene Expression

90 With alternative growth conditions for the microarray experiments and a more sensitive motif identi

91 problem in genome research, particularly in microarray experiments and genomewide association studie

92 igos) that can be used in PCR primer design, microarray experiments and genomic library screening.

93 mathematical model for the measured data in microarray experiments and on the basis of this model pr

94 the combination of gene expression data from microarray experiments and promoter sequence analysis of

95 trate the effectiveness of error model based microarray experiments and propose this as a general str

96 Microarray experiments and semiquantitative real-time re

97 promoted as a standard format for describing microarray experiments and the data they produce.

98 mat of the data collected in gene expression microarray experiments and therefore can be immediately

99 erize several of the genes identified in the microarray experiments and uncovered novel regulators of

100 for instance, tumor classification in a DNA microarray experiment, and prediction in the context of

101 (s) of molecules, such as those derived from microarray experiments, and either overlay these molecul

102 Animal models, gene expression microarray experiments, and functional studies in cell l

103 in computer science to mine lists of genes, microarray experiments, and gene networks to address que

104 'Miracle-Wheat.' mRNA in situ hybridization, microarray experiments, and independent qRT-PCR validati

105 Time-course microarray experiments are capable of capturing dynamic

106 Microarray experiments are commonly afflicted with satur

107 Maximum-likelihood methods for multiple microarray experiments are developed, and likelihood-rat

108 Measurements of gene expression from microarray experiments are highly dependent on experimen

109 so-called 'loop designs' for two-channel DNA microarray experiments are more efficient, biologists co

110 ts from tandem mass spectrometry (MS/MS) and microarray experiments are presented.

111 Though microarray experiments are very popular in life science

112 Identified in microarray experiments as displaying flagellar gene expr

113 challenges arising in the analysis of tiling microarray experiments as open problems to the scientifi

114 differential methylation hybridization (DMH) microarray experiments as well as other effects inherent

115 eloped KUTE (Karmanos Universal daTabase for microarray Experiments), as a plug-in for BASE 2.0 that

116 -friendly software for analyzing time series microarray experiments (BATS).

117 the genes that emerged as significant in the microarray experiment, but were previously of unknown fu

118 gene expression data from a prostate cancer microarray experiment by constructing confidence bands f

119 lysis provides a powerful tool for analyzing microarray experiments by combining data from multiple s

120 ADAPT was designed to help interpret microarray experiments by providing a means to explore t

121 The information from multiple microarray experiments can be integrated in an objective

122 Microarray experiments can be used to help study the rol

123 , competitive cross-species hybridization of microarray experiments, can characterize the influence o

124 er of clones with replications, a customized microarray experiment carrying just a few hundred genes

125 ng Chromatin ImmunoPrecipitation followed by microarray experiments (ChIP-chip).

126 We used expression data from two microarray experiments, cis-regulatory motif elucidation

127 Microarray meta-analysis using 13 microarray experiments combined with empirically defined

128 Therefore, there is no doubt that microarray experiments, combined with bioinformatics, wi

129 the impact of this substrate on a one-color microarray experiment comparing gene expression in two d

130 Instead, DNA microarray experiments comparing gene expression in naiv

131 rage in a MIAME (Minimal Information about a Microarray Experiment) compliant fashion, and allows dat

132 e basis of leaf abscission were defined, and microarray experiments conducted on these genotypes and

133 differential gene expression for replicated microarray experiments conducted under two conditions.

134 these relationships using the data from two microarray experiments containing over three million pro

135 As the cost of microarray experiments continues to fall, the temporal r

136 This is probably owing to the noisy data of microarray experiments coupled with small sample sizes o

137 BarleyExpress is a web-based microarray experiment data submission tool for BarleyBas

138 Microarray experiments demonstrated that the loss of Stb

139 Microarray experiments demonstrated that these RLKs cont

140 Moreover, use of data from expression microarray experiments demonstrated the generality of PL

141 In expression microarray experiments, depletion of NMNAT-1 or PARP-1 a

142 The power of a microarray experiment derives from the identification of

143 E-ML Babel bars the unencumbered exchange of microarray experiment descriptions couched in MAGE-ML.

144 Microarray experiments designed to identify genes differ

145 he gene expression profile identified in the microarray experiment did not exist prior to cell sortin

146 Microarray experiments did not reveal a close correspond

147 Genomic tiling microarray experiments differ in that probes that span a

148 In DNA microarray experiments, discovering groups of genes that

149 mentation of the Minimal information about a microarray experiment document provides several lessons

150 tion is often not valid in the analysis of a microarray experiment due to systematic biases in the me

151 The datasets can come from microarray experiments (e.g. genes induced in each exper

152 footprinting of selected sequences from each microarray experiment enabled quantitative prediction of

153 Additional microarray experiments establish that Yap 4 and 6 regula

154 cerevisiae were previously identified using microarray experiments focused on sphingolipid-dependent

155 sis of inflammatory myopathies, we performed microarray experiments followed by real-time PCR and imm

156 -humanized (tg-CYP2D6) mice was compiled via microarray experiments followed by real-time quantitativ

157 Genome-scale microarray experiments for comparative analysis of gene

158 In the context of microarray experiments for detecting differentially expr

159 fect of pooling samples on the efficiency of microarray experiments for the detection of differential

160 es (previously yielding only noise levels in microarray experiments) for genome-wide microarray "sign

161 Microarray experiments frequently produce multiple missi

162 ovides terms for annotating all aspects of a microarray experiment from the design of the experiment

163 ck function using existing single time point microarray experiments from a recombinant inbred line po

164 pendia of microarray experiments, we present Microarray Experiment Functional Integration Technology

165 sed on observed results from high throughput microarray experiments, gene sequences, and next-generat

166 Microarray experiments generate a high data volume.

167 Quality control of a microarray experiment has become an important issue for

168 The need for normalization in microarray experiments has been well documented in the l

169 Using genomic DNA as common reference in microarray experiments has recently been tested by diffe

170 Gene expression data extracted from microarray experiments have been used to study the diffe

171 Microarray experiments have now revealed abundant copy-n

172 In the analysis of time course microarray experiments, I found cell cycle- and ribosome

173 A microarray experiment identified two genes necessary for

174 This microarray experiment identified>2,000 genes that are 1.

175 Finally, genome-wide microarray experiments identified additional Trh targets

176 Microarray experiments identified cyclin-dependent kinas

177 transcriptional induction of KAR3 and CIK1, microarray experiments identified many genes regulated b

178 We motivate the approach by a comparative microarray experiment in which clones of a cell were sin

179 from comparative genome hybridization (CGH) microarray experiments in fungi and bacteria.

180 erved" sensitivity of each method on typical microarray experiments in which the majority (or all) of

181 Recent microarray experiments in which we compared the gene exp

182 All key components of a microarray experiment, including quality control, normal

183 rent imputation methods on multiple types of microarray experiments, including time series, multiple

184 In a series of microarray experiments, increased Stat3 mRNA levels in t

185 As the number of publically available microarray experiments increases, the ability to analyze

186 transcription polymerase chain reaction, and microarray experiments indicate that the abundance of Ti

187 Microarray experiments indicated that culture of RINm5F

188 Microarray experiments indicated that the levels of seve

189 Microarray experiments indicated that the Toll/IL-1R dom

190 MAGE-ML variants for improved interchange of microarray experiment information.

191 In microarray experiments investigators sometimes wish to p

192 However, in microarray experiments involving the analysis of very la

193 A microarray experiment is a multi-step process, and each

194 r, often the sample size for each individual microarray experiment is small.

195 d the analysis methods used, the result of a microarray experiment is, in most cases, a list of diffe

196 enerating the expression pattern observed in microarray experiments is a major challenge.

197 finding biological and clinical markers from microarray experiments is problematic due to the large n

198 A common objective of microarray experiments is the detection of differential

199 One of the biggest problems facing microarray experiments is the difficulty of translating

200 A common task posed by microarray experiments is to infer the binding site pref

201 roughput experimental data, for example from microarray experiments, is currently seen as a promising

202 the high cost and enhance the consistency of microarray experiments, it is often desirable to strip a

203 hod, the MLE method applied to data from the microarray experiment leads to an increase in the number

204 Microarray experiments measure changes in the expression

205 Microarray experiments measure complex changes in the ab

206 y founded on the Minimum Information About a Microarray Experiment (MIAME) principles that stores MIA

207 odeled after the Minimum Information About a Microarray Experiment (MIAME) specification for microarr

208 e database has a minimum information about a microarray experiment (MIAME)-compliant infrastructure t

209 andards such as 'Minimum Information About a Microarray Experiment' (MIAME).

210 In microarray experiments, missing entries arise from blemi

211 Here, chromatin immunoprecipitation-microarray experiments normalized to general nucleosome

212 Microarray experiments often involve hundreds or thousan

213 or genes repeatedly implicated in functional microarray experiments (often publicly available).

214 We tested MotifModeler using data from a microarray experiment on the effects of interferon-alpha

215 We performed microarray experiments on 31 postmenopausal endometrial

216 esents the unequalled possibility to perform microarray experiments on most of the sequenced organism

217 s this problem, we performed protein binding microarray experiments on representatives of canonical T

218 CARRIE to six sets of publicly available DNA microarray experiments on Saccharomyces cerevisiae.

219 ) positional candidates, literature reviews, microarray experiments, ontological or even meta-data, m

220 that could find routine use in the design of microarray experiments or in post-experiment assessment.

221 uality control using five publicly available microarray experiments: outlier removal and array weight

222 A microarray experiment over two days of growth in light-d

223 l technique to cluster data from time course microarray experiments performed across several experime

224 A dose-response microarray experiment predicted an apparent dissociation

225 In microarray experiments, probe design is critical to the

226 Many microarray experiments produce temporal profiles in diff

227 transcriptional programmes, and whole-genome microarray experiments promise to reveal this complexity

228 the differential analysis of gene expression microarray experiments provided a set of candidate genes

229 We used LFDR to compare different microarray experiments quantitatively: (i) Venn diagrams

230 user-friendly, open-access database of mRNA microarray experiments relevant to allergic airway infla

231 lues, however, most down-stream analyses for microarray experiments require complete data.

232 ports the MIAME (Minimum Information About a Microarray Experiment) requirements and stores well-anno

233 Obtaining physiological insights from microarray experiments requires computational techniques

234 Epistasis and microarray experiment results were consistent with a rol

235 Microarray experiments revealed that glucose transporter

236 Protein microarray experiments revealed that more than 100 human

237 Microarray experiments revealed that the 2 proteins redu

238 rect genotypes, as confirmed by fluorescence microarray experiments run in parallel.

239 Therefore, microarray experiments should be replicated several time

240 signals has been questioned recently because microarray experiments show that the addition of signals

241 Both simulated and real microarray experiments show that the PLS-based approach

242 ese conditions, where motility is shut down, microarray experiments showed an increased RNA signal fo

243 Microarray meta-analysis of 304 independent microarray experiments showed that ADM is elevated in sm

244 of expression data from two single-dye cDNA microarray experiments showed that ESTs whose sequences

245 rds, such as the minimum information about a microarray experiment standard, tools are required to as

246 Recent microarray experiments suggested that Burkholderia xenov

247 Microarray experiments suggested that RitR represses Fe

248 Results from these microarray experiments support the idea that the VicR RR

249 validated this new statistical approach on a microarray experiment that captures the temporal transcr

250 Recent "tiling" microarray experiments that assay transcription at regul

251 eport results from preliminary transcription microarray experiments that revealed two previously unkn

252 ts exist with different severity in all cDNA microarray experiments that we analyzed.

253 ata from chromatin immunoprecipitation-based microarray experiments, that SHR regulates the spatiotem

254 In microarray experiments the numbers of replicates are oft

255 In phenotype microarray experiments, the glpG mutant exhibited a slig

256 ble in the public domain for the analysis of microarray experiments, this is not the case for qPCR.

257 Gene expression levels are obtained from microarray experiments through the extraction of pixel i

258 other SA-independent systems, we designed a microarray experiment to distinguish between transcripto

259 e performed chromatin immunoprecipitation on microarray experiments to assay binding of RNA polymeras

260 r competitive cross-species hybridization of microarray experiments to characterize the effect of the

261 , quantitative trait loci (QTL) analysis and microarray experiments to demonstrate that 'genetical ge

262 MPP analyses flat file output from microarray experiments to determine the probability of t

263 color platform, this study uses ten spike-in microarray experiments to evaluate the relative effectiv

264 In this study, we have performed microarray experiments to follow the fate of all origins

265 ates patterns of gene expression in a set of microarray experiments to functional groups in one step.

266 process at the molecular level, we used DNA microarray experiments to identify a set of 1294 age-reg

267 ally, we analyze multiple publicly available microarray experiments to show that statistically valida

268 Microarray experiments typically analyze thousands to te

269 Microarray experiments typically involve washing steps t

270 ne expression in an interwoven loop designed microarray experiment using a 4k-cDNA array.

271 and the detection capabilities of a standard microarray experiment using a photonic crystal (PC) surf

272 Our approach should be readily applicable to microarray experiments using other types of molecular bi

273 synthase (TPS) genes identified through the microarray experiments was confirmd using real-time RT-P

274 ubset of 15 of 828 genes identified by these microarray experiments was independently confirmed by qu

275 RNA for the microarray experiments was isolated from both wild-type

276 In microarray experiments, we found that 196 of these trans

277 in immunoprecipitation (ChIP) sequencing and microarray experiments, we further demonstrate that Ncoa

278 Now, using data from published microarray experiments, we have identified a histone dem

279 d in the analysis of such large compendia of microarray experiments, we present Microarray Experiment

280 In two-color microarray experiments, well-known differences exist in

281 t step, gene expression levels measured from microarray experiments were assigned to two different cl

282 Multiple microarray experiments were combined with quantitative P

283 Transcriptome-wide microarray experiments were conducted on 42 samples (TTI

284 Complementary DNA microarray experiments were first performed to identify

285 find out if the processes highlighted by the microarray experiments were in fact under iron and/or Fu

286 he different sources of variation arising in microarray experiments were not distinguished.

287 resolve the time course of gene expression, microarray experiments were performed at narrow interval

288 Microarray experiments were performed in a gamma-proteob

289 All microarray experiments were performed in duplicate using

290 Microarray experiments were performed on 50 samples of h

291 a critical initial step in the analysis of a microarray experiment, where the objective is to balance

292 enes in the Calvin cycle from 95 Arabidopsis microarray experiments, which revealed a consistent gene

293 analysis and interpretation of an Affymetrix microarray experiment will be in error.

294 The method is applied to a microarray experiment with 60 F(2) mice measured for 25

295 Previous microarray experiments with an exoS96::Tn5 mutant indica

296 Microarray experiments with cti6 mutants grown under iro

297 Microarray experiments with human umbilical vein endothe

298 ulated for adjacent gene pairs from over 474 microarray experiments with MTB RNA.

299 Microarray experiments with plants overexpressing trunca

300 We describe two sets of microarray experiments with RNA from two different biolo