ライフサイエンスコーパス: microarray data

1 ressed miRNAs in human cancers obtained from microarray data.

2 as validated by using independent, published microarray data.

3 ical management of cancer in the presence of microarray data.

4 f false positives and false negatives in the microarray data.

5 heir magnitude, the first such model for SNP microarray data.

6 m gene selection to infer such networks from microarray data.

7 plicitly models the possible outliers in the microarray data.

8 t germ cell-specific expression from gonadal microarray data.

9 in an independent set of publicly available microarray data.

10 o counteract the presence of outliers in the microarray data.

11 lobal test in both simulation and a diabetes microarray data.

12 een developed to identify bimodal genes from microarray data.

13 ictive clinical tool using published patient microarray data.

14 oth CCNE2 and CDC6 were downregulated in the microarray data.

15 el algorithm, VIPR, for analyzing diagnostic microarray data.

16 essment of model predictions against patient microarray data.

17 reus, Saccharomyces cerevisiae and in silico microarray data.

18 performance using alternative sequencing and microarray data.

19 ultiple etiologies by exploring high quality microarray data.

20 f known relevant genes in large compendia of microarray data.

21 uency) copy-number variants (CNVs) using SNP microarray data.

22 t pair-wise synergy in simulation and cancer microarray data.

23 can also be applied to stabilize variance in microarray data.

24 designed for archiving and analyzing protein microarray data.

25 ene regulatory networks from gene expression microarray data.

26 d apply it to the integration of RNA-seq and microarray data.

27 performed for statistical assessment of the microarray data.

28 prehensive analysis report for their protein microarray data.

29 ine part of the analysis of high-dimensional microarray data.

30 fective method to estimate purities from the microarray data.

31 se when combining batches of gene expression microarray data.

32 omes reduced to low-coverage sequence and HD microarray data.

33 space, rather than in the original space of microarray data.

34 identified from the analyses of leafy spurge microarray data; (2) 3 orthologs of Arabidopsis "general

35 e, we have integrated four public expression microarray data (320 samples) from the Gene Expression O

36 We screened C. elegans microarray data [5, 6] to identify male germline-enriche

37 ere constructed from all currently available microarray data, 90% phenotype prediction accuracy, or t

38 and standardize Meta-Analysis of Affymetrix Microarray Data analysis (MAAMD) in Kepler.

39 own to reduce overfitting noises involved in microarray data analysis and discover functional gene se

40 e have also created a pathway module for the microarray data analysis portal ArrayAnalysis.org that c

41 Microarray data analysis revealed striking correlations

42 Microarray data analysis showed a pleiotropic effect of

43 munohistochemistry (IHC) staining and public microarray data analysis showing that DACH1 was higher i

44 We developed an R package for resequencing microarray data analysis that integrates a novel statist

45 alysis is an invaluable tool for exploratory microarray data analysis, as it organizes the data into

46 lthough FDR control is frequently applied to microarray data analysis, gene expression is usually cor

47 ntified important proteins were confirmed by microarray data analysis.

48 and salt stresses, as indicated by previous microarray data analysis.

49 It uses the microarray data and a 28-trait image array to evaluate e

50 optimize the strength of interactions using microarray data and an artificial neural network fitness

51 logy will facilitate re-analysis of archived microarray data and broaden the utility of the vast quan

52 chromatin immunoprecipitation sequencing and microarray data and DNase I hypersensitive site sequenci

53 al-inverse-Wishart priors based on discarded microarray data and examine the performance on synthetic

54 r from the inclusion of excessive 'noise' in microarray data and false-positives in binding data, esp

55 anscriptional responses to TDB/TDM, we mined microarray data and identified early growth response (Eg

56 nthesis in Arabidopsis thaliana, we screened microarray data and identified transcriptional upregulat

57 QRT-PCR analysis confirmed the microarray data and revealed expression patterns of some

58 pL, and bcrA-like were carried out to verify microarray data and showed the same level of up- or down

59 of antibody epitope prediction from peptide microarray data and shows utility in analyzing phage dis

60 Through analyses of our microarray data and the published Arabidopsis (Arabidops

61 tal cancer (CRC) metastasis was suggested by microarray data and validated by the survey of 120 patie

62 numerous transfer RNAs (tRNAs) dominated the microarray data and were validated on RNA gel blots.

63 Additionally, R objects, for microarray data, and binary alignment format files, for

64 tion model used for background correction of microarray data, and modified it to formulate an error c

65 low-coverage sequence and high-density (HD) microarray data, and remained high even with a read erro

66 ds developed for bulk RNA sequencing or even microarray data, and the suitability of these methods fo

67 dicting the survival of cancer patients from microarray data, and to classification of obese and lean

68 With Arabidopsis (Arabidopsis thaliana) microarray data annotated to the PathoPlant database, 73

69 the methods for GSA previously developed for microarray data are based on the assumption that genes w

70 ream analysis tools previously restricted to microarray data are now available for RNA-seq as well.

71 Microarray data are used to determine which genes are ac

72 s from multiple tissues when log-transformed microarray data are used; (ii) estimation of both tumor

73 ficity are similar to those calculated using microarray data as a reference.

74 ological perspective, we used a set of yeast microarray data as a working example, to evaluate the fu

75 ely estimate absolute expression levels from microarray data, at both gene and transcript level, whic

76 Finally, reassessing previous C. pneumoniae microarray data based on codon content, we found that up

77 r 12 candidate transcripts selected from the microarray data based upon fold change and biological re

78 component analysis and k-means clustering of microarray data, because our traditional cardiac and ser

79 athway analysis strategy comparing miRNA and microarray data between three mouse models and human don

80 f our approach on real world gene expression microarray data by applying it to existing data from amy

81 istical methods that have been developed for microarray data can be applied to RNA-Seq data, they are

82 t feature selection approaches developed for microarray data cannot handle multivariate temporal data

83 h was developed in ecology and sociology, to microarray data (CCA on Microarray data, CCAM).

84 gy and sociology, to microarray data (CCA on Microarray data, CCAM).

85 Microarray data clustering revealed a striking pattern o

86 entification of differential exon use in the microarray data, clustering of exon inclusion/exclusion

87 Using gene microarray data collected in a large scale serially samp

88 ignificantly differentially expressed in the microarray data collected under the differing conditions

89 thods that are applied in biology to analyze microarray data, concerns regarding the compatibility of

90 Publicly available microarray data confirmed differential expression of all

91 RNA-Seq and qRT-PCR, but not microarray data, confirmed the expected reversal of SPAR

92 Similarly, human breast cancer microarray data demonstrated that high LOX/low GATA3 exp

93 Analysis of microarray data demonstrated that the transcriptome of C

94 ed to identify over-represented processes in microarray data derived from various disease states.

95 serum assays, including 2-way comparison of microarray data, did not lead to the identification of a

96 hidden within multilayered immunosignaturing microarray data due to its fundamental mathematical prop

97 elation coefficients between the RNA-Seq and microarray data each exceeded 0.80, with 66%~68% overlap

98 e regulatory network from publicly available microarray data, employing steps to enrich for physiolog

99 Genome-wide microarray data enable unbiased documentation of alterat

100 Many cleaning approaches for microarray data exist, however these methods are aimed t

101 Analysis of raw microarray data files (e.g. Affymetrix CEL files) can be

102 Mandatory deposit of raw microarray data files for public access, prior to study

103 pplied association mining on a set of glycan microarray data for 211 influenza viruses from five host

104 Microarray data for 24-week-old BKS db/db and db/+ mouse

105 We conducted a meta-analysis of microarray data for 240 NFE2L2-mediated genes that were

106 quencing data and Affymetrix gene expression microarray data for 30 breast cancer cell lines represen

107 ve seed-specific expression, as indicated by microarray data for Arabidopsis.

108 the RT-qPCR validation were in line with the microarray data for both miRNAs, and statistically signi

109 Furthermore, using available microarray data for gene expression, we show that in bot

110 = 84), ELISAs of blood/BM sera (n = 41), and microarray data for mRNA levels (n = 261) were performed

111 port detailed structural analysis and glycan microarray data for recombinant hemagglutinins from A(H6

112 Given the microarray data for the alterations in gene expression,

113 n RBPs and RIP-ChIP (RNP immunoprecipitation-microarray) data for 69 yeast RBPs to construct a networ

114 proteins involved in lamination, we utilized microarray data from 13 subtypes to identify differentia

115 pse-free survival (RFS) were evaluated using microarray data from 148 patients with stage I lung aden

116 Here, we analyzed publicly available microarray data from 16 diverse skin conditions in order

117 Using microarray data from 16 ferret samples reflecting cystic

118 Microarray data from 207 AML patients confirmed a greate

119 This prompted us to analyze microarray data from 261 patients from a third cohort.

120 Using publicly available microarray data from 46 primary human melanomas (GSE1560

121 tern blot, and immunofluorescence), analyzed microarray data from 99 patients with IPF and 43 control

122 iochemical interactions, our method utilizes microarray data from a group of mutants for its construc

123 demonstrate how mining publically available microarray data from a range of skin disorders can eluci

124 To this end, we performed a meta-analysis of microarray data from a variety of cytokinin-treated samp

125 dules and schizophrenia was replicated using microarray data from an independent tissue collection.

126 Microarray data from both cell types showed significant

127 Microarray data from cell lines of Non-Small Cell Lung C

128 outcome prediction in patient cohorts using microarray data from diagnosis specimens.

129 Comparative analysis of microarray data from females after mating and after 20E

130 Analysis of microarray data from healthy human intestine further rev

131 Mining of microarray data from human tumor data sets revealed that

132 cell-specific patterns of gene expression in microarray data from mammalian gonads, specifically duri

133 By interpreting microarray data from MATa cells, MATa/alpha cells, a sta

134 Using microarray data from orbitofrontal cortex of control sub

135 istent with these findings, meta-analysis of microarray data from over 4,000 breast cancer patients r

136 deletions and uniparental disomy) using SNP microarray data from over 50,000 subjects recruited for

137 g glucose through the use of gene expression microarray data from peripheral blood samples of partici

138 ighted gene coexpression network analysis on microarray data from singing zebra finches to discover g

139 Microarray data from the Cftr-deficient colon and the sm

140 By re-analysis of existing microarray data from the FGF8, Lim1 and Wnt4 knockouts,

141 Expression microarray data from the kidney cortex and medulla, live

142 Pathways analysis of microarray data from the mouse brain revealed gene netwo

143 to generate Illumina RNA-seq and Affymetrix microarray data from the same liver samples of rats expo

144 oinformatic analysis was performed using DNA microarray data from two experimental formats: (1) ventr

145 ChIP-sequencing results with gene expression microarray data from unloaded muscle to map several dire

146 We analysed gene expression microarray data from whole blood samples from 228 multip

147 is system in four cohorts and extracted from microarray data (GeneChip) in the other two.

148 comprehensively analyzed RNA sequencing and microarray data generated by the Immunological Genome Pr

149 ful analytical tool using publicly available microarray data generated exclusively from Arabidopsis t

150 s to compare parallel paired-end RNA-Seq and microarray data generated on 5-azadeoxy-cytidine (5-Aza)

151 ed cytosine (5C), 5mC and 5hmC from Infinium microarray data given the signal intensities from the ox

152 The HS microarray data guided the selection of compounds that c

153 The recent advent of high-throughput microarray data has enabled the global analysis of the t

154 he existing methods for analyzing diagnostic microarray data has the capacity to specifically identif

155 Custom microarray data have been generated using RNA isolated f

156 However, recent microarray data have indicated that nuc is under the con

157 Through meta-analysis of microarray data, here we nominate nephroblastoma overexp

158 n silico analysis exploiting mNK cell subset microarray data, highlighting various genes and microRNA

159 Additionally, FungiDB contains cell cycle microarray data, hyphal growth RNA-sequence data and yea

160 The microarray data identified 454 candidate genes with expr

161 Analysis of published microarray data identified a network of genes up-regulat

162 chain reaction, and motifADE analysis of the microarray data identified potential FK506-mediated path

163 Re-analysis of genome-wide microarray data in 9 patient blood and 10 skin samples r

164 Bioinformatic analyses of microarray data in ceh1 plants established the overrepre

165 a in various primary tumors, gene expression microarray data in over 1000 cancer cell lines and prote

166 The interpretation of microarray data in the context of coexpression network a

167 a diverse collection of PDAC gene expression microarray data, including data from primary tumor, meta

168 Analysis of rice microarray data indicated higher expression levels for O

169 Our previous microarray data indicated sphingosine 1-phosphate (S1P)

170 Pathway analyses of mRNA expression microarray data indicated that cells exposed to C4BP and

171 Analyses of breast cancer clinical microarray data indicated that high expression of SND1 i

172 Biolog Phenotype MicroArray data indicated that mmp deletion increased su

173 s was determined by FACS analysis.Affymetrix microarray data indicated that NuMA was overexpressed in

174 Analysis of previously published microarray data indicated unscheduled transcription of a

175 Comparison between the ChIP-seq and microarray data indicates that FHY3 quickly regulates th

176 antly impacted in a given condition based on microarray data is a crucial step in current life scienc

177 However, finding relevant microarray data is complicated by the large number of av

178 he majority of the valuable original protein microarray data is still not publically accessible.

179 Removing systematic noise from microarray data is therefore crucial.

180 der to reduce the impact of batch effects on microarray data, Johnson, Rabinovic, and Li developed Co

181 is of Microarrays (SAM) or Linear Models for Microarray Data (LIMMA) for processing cDNA microarrays,

182 However, the information from microarray data may not be fully deciphered through the

183 We generate microarray data measuring time-dependent gene-expression

184 results also supported by Oncomine analyses, microarray data (n=2878) and genomic data from paired tu

185 Gene ontology analysis of cDNA microarray data obtained after BMP9 treatment of primary

186 ounding effects of ITH using gene expression microarray data obtained from multiple tumour regions of

187 Using time-series microarray data of Arabidopsis thaliana infected with Ps

188 Using GMine we reanalyzed proteome microarray data of host antibody response against Plasmo

189 On microarray data of hundreds of deletion mutants in two,

190 also compared with the previously published microarray data of Li1 ovule tissues.

191 involved in SA-induced folate accumulation, microarray data of responsive genes in Arabidopsis were

192 Analysis of microarray data on gene expression and methylation allow

193 -small cell lung cancer (NSCLC), we analyzed microarray data on gene expression and methylation.

194 etwork construction, we generate independent microarray data on selected deletion mutants to prospect

195 s and heteroskedasticity across 19 groups of microarray data on the sign and magnitude of gene-to-gen

196 We used microarray data on whole blood from two independent case

197 2 pathways by RNA-Seq data only, and none by microarray data only.

198 ditionally, criteria such as comparison with microarray data or a number of known polymorphic sites h

199 Functional enrichment analysis of the microarray data predicted that multiple biological funct

200 isease susceptibility, while gene expression microarray data provide genome-wide transcriptional prof

201 lity of our approach with both simulated and microarray data; random graphs and weighted (partial) co

202 glucuronidase) assays and publicly available microarray data revealed a differential spatio-temporal

203 Our microarray data revealed a distinct gene expression prof

204 Integration analysis of mRNA-miRNA microarray data revealed a potential role of 51 dysregul

205 Evaluation of neuroblastoma patient microarray data revealed an association between TGFBR3,

206 Microarray data revealed changes in expression of 504 ge

207 In humans, microarray data revealed declines in E2F3 and IGF2 expre

208 Analysis of microarray data revealed that adenine stimulation led to

209 Mining public microarray data revealed that NCOR1-targeted genes were

210 A thorough analysis of this microarray data revealed unique patterns of gene express

211 d and Consensus Clustering Analysis Tool for Microarray Data (SC(2)ATmd) is a MATLAB-implemented appl

212 ost cells validates the previously published microarray data set demonstrating feed-forward control o

213 Second, a large microarray data set of prostate cancer was used to asses

214 scription factor analysis was performed in a microarray data set profiled in four different brain reg

215 Thus a clinical classifier weighted with microarray data set results in significantly improved di

216 paper, we reanalyzed a zebrafish (D. rerio) microarray data set using GeneSpring and different diffe

217 We demonstrate that for a specific microarray data set using the Human HG_U133A Affymetrix

218 We therefore analyzed our microarray data set, cellular proteomes of separated lyt

219 Using a public microarray data set, we identified via TEAK linear sphin

220 data set, and a combined publicly available microarray data set.

221 licly available glioblastoma gene expression microarray data set.

222 cuity in a publicly available small molecule microarray data set.

223 Integrative analysis of ChIP-seq and microarray data sets also reveals a consistent role of N

224 tperforms classical algorithms developed for microarray data sets as well as recent approaches design

225 project uses the abundant publicly available microarray data sets combined with a suite of single-arr

226 lly expressed at significant levels in the 5 microarray data sets compared, providing new insights in

227 pport of our in vitro data, analysis of mRNA microarray data sets demonstrated that high levels of FK

228 med in silico analysis of publicly available microarray data sets from prostate or breast carcinomas.

229 including CCND2, hTERT, and GCLC Analysis of microarray data sets further demonstrated that MUC1 leve

230 Using whole-genome microarray data sets of the Immunological Genome Project

231 ed TEAK with experimental studies to analyze microarray data sets profiling stress responses in the m

232 Meta-analyses of relevant microarray data sets revealed the hematopoietic stem cel

233 Finally, an analysis of paired RNA-seq/microarray data sets suggests that no or modest trimming

234 We interrogated known microarray data sets to define NAMPT knockdown-influence

235 We analyzed microarray data sets to identify a subset of genes whose

236 functional relationships between genes from microarray data sets using rule-based machine learning.

237 In two independent microarray data sets, 77% to 100% of tumors had substant

238 ces, such as the Allen Mouse Brain Atlas and microarray data sets, by providing quantitative expressi

239 By a meta-analysis of DNA stress microarray data sets, three family members of the SIAMES

240 e performed against publicly available human microarray data sets.

241 IGF2BP family members was first evaluated by microarray data sets.

242 Two independent microarray data-sets from human renal allograft biopsies

243 Transcriptome in silico analysis of the microarray data showed major metabolomic variations in k

244 Microarray data showed that 324 genes were up-regulated

245 Analysis of published microarray data showed that AK4 was upregulated in lung

246 In this study, microarray data showed that either addition of oxygen or

247 Analysis of ovarian cancer gene microarray data showed that higher expression of Nectin-

248 Analysis of microarray data showed that iron deficiency in utero res

249 of the diffuse large B-cell lymphoma patient microarray data showed that miR-155 expression is invers

250 Comparison of microarray data showed that NF-kappaB was among the tran

251 Systematic analysis of tissue-profiling microarray data showed that the zinc transporter ZIP12 (

252 Our in silico analysis of public microarray data shows that auxin and glutathione redox s

253 ated clinical literature and gene expression microarray data stored in large international repositori

254 Accurate differential analysis of microarray data strongly depends on effective treatment

255 Microarray data suffers from several normalization and s

256 Microarray data suggest that SDSE degraded host tissue p

257 ymidine kinase genes (AtTK1a and AtTK1b) and microarray data suggest they might have redundant roles.

258 Pathway analysis of microarray data suggested activation of the p53 and reti

259 Gene ontology analysis of microarray data suggested that the beta-catenin-independ

260 Thus repeat annotation of gene expression microarray data suggests that a complex interplay betwee

261 Further analysis of the microarray data suggests that B. thailandensis, when exp

262 Lastly, semi-quantitative model analysis via microarray data superimposed onto the model with a score

263 Our microarray data support our histological findings and re

264 We performed a review of public microarray data that revealed a significant down-regulat

265 ustom 'Ae. aegypti detox chip' and validated microarray data through RT-PCR comparing susceptible and

266 We then used microarray data to develop classifiers that assigned ant

267 and an R function which uses DNA methylation microarray data to infer tumor subtypes with the conside

268 In this study we have explored microarray data to investigate the expression pattern of

269 n genetic data from GWAS and gene expression microarray data to reposition drugs for PD.

270 constructed gene co-expression networks from microarray data to study large-scale transcriptional pat

271 nomes pathway enrichment cluster analyses of microarray data using wild-type and c-Jun-deleted macrop

272 Microarray data, validated against direct RNA sequencing

273 Arabidopsis seed coat microarray data was analysed for genes expressed in the

274 Functional annotation analysis of DNA microarray data was consistent with depressed innate imm

275 t format and specificity which correlated to microarray data was demonstrated.

276 ng independent normal data and one involving microarray data), we show that the proposed method, when

277 Using time-series microarray data, we analyzed the temporal behavior of mR

278 Using existing microarray data, we defined 24 circadian time phase grou

279 Arabidopsis thaliana) homologs and available microarray data, we identified 60 candidate genes for co

280 Using RNAseq and microarray data, we identified a set of genes that are h

281 correlation of chemical data and phylogenic microarray data, we identified several bacteria that cou

282 In this work, using microarray data, we investigate the feasibility and effe

283 Microarray data were analyzed using efficiency analysis

284 gene expression analysis was performed, and microarray data were assessed by Ingenuity Pathways Anal

285 Microarray data were further validated by immunoblotting

286 n the present study, the whole transcriptome microarray data were generated from peripheral blood mon

287 signed for DEG identification in RNA-Seq and microarray data, were applied to compare the cross-platf

288 methods for the analysis of DNA methylation microarray data, which account for tumor purity.

289 study the rewiring of gene networks through microarray data, which is becoming an important compleme

290 r mechanisms, we analyzed publicly available microarray data, which revealed a developmentally coordi

291 Furthermore, coexpression analysis of microarray data, which reveals the dynamics of host resp

292 from this work and the predictions based on microarray data will help explore novel metabolic proces

293 network-type analyses along with time series microarray data will lead to advancements in our underst

294 e algorithms that reconcile case-control DNA microarray data with a molecular interaction network by

295 amined the behaviors of different methods to microarray data with different properties, and whether t

296 Through integration of microarray data with genome-wide histone modification Ch

297 StRAP houses multi-cancer microarray data with major emphasis on radiotherapy stud

298 We investigate the impact of augmenting microarray data with semantic relations automatically ex

299 By correlating clinical microarray data with the patients' outcome, a link betwe

300 ties allow users to analyse both RNA-seq and microarray data with very similar pipelines.