ライフサイエンスコーパス: microarray data

1 using Biobank of Karolinska Endarterectomies microarray data.

2 ene regulatory networks from gene expression microarray data.

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

4 performed for statistical assessment of the microarray data.

5 prehensive analysis report for their protein microarray data.

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

7 fective method to estimate purities from the microarray data.

8 se when combining batches of gene expression microarray data.

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

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

11 as validated by using independent, published microarray data.

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

13 expression over time, using bulk RNA-seq or microarray data.

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

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

16 essfully validated the original EBV proteome microarray data.

17 plicitly models the possible outliers in the microarray data.

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

19 ions were mainly made in trials and based on microarray data.

20 in an independent set of publicly available microarray data.

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

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

23 een developed to identify bimodal genes from microarray data.

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

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

26 visualize, analyze, present and mine glycan microarray data.

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

28 ressed miRNAs in human cancers obtained from microarray data.

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

30 ictive clinical tool using published patient microarray data.

31 essment of model predictions against patient microarray data.

32 performance using alternative sequencing and microarray data.

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

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

35 designed for archiving and analyzing protein microarray data.

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

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

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

39 To facilitate analysis of glycan microarray data alongside protein structure, we have bui

40 RNA-seq and microarray data analyses of the putative GLCAT genes rev

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

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

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

44 Microarray data analysis showed a pleiotropic effect of

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

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

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 nes in mouse ST and FT fibers, mining of our microarray data and a qPCR analysis of quadriceps specim

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

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

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

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

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

56 However, it is difficult to interpret microarray data and identify structural determinants pro

57 ords, and subtypes were determined by tissue microarray data and pathology reports.

58 Cutaneous lupus erythematosus lesional skin microarray data and RNA sequencing data from SLE keratin

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

80 Microarray data clustering revealed a striking pattern o

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

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

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

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

85 Publicly available microarray data confirmed differential expression of all

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

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

88 Analysis of microarray data demonstrated that the transcriptome of C

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

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

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

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

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

94 Genome-wide microarray data enable unbiased documentation of alterat

95 Available microarray data enabled direct comparison of polygenic r

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

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

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

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

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

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

102 base query, and allows users to upload their microarray data for analysis.

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

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

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

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

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

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

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

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

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

112 Using microarray data from 16 ferret samples reflecting cystic

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

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

115 mans, we screened whole exome sequencing and microarray data from a clinical cohort.

116 Applying our method to microarray data from a fracture healing study revealed d

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

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

119 is, we performed gene enrichment analysis of microarray data from adipose tissues of adult rabbits.

120 along with high-resolution postmortem brain microarray data from Allen Brain Atlas (donors n = 6) fr

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

122 Microarray data from both cell types showed significant

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

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

125 Although microarray data from diseased patient kidneys and fibrot

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

127 Analysis of microarray data from healthy human intestine further rev

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

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

130 A novel meta-analysis of microarray data from OA patient tissue was used to creat

131 Using microarray data from orbitofrontal cortex of control sub

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

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

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

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

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

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

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

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

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

141 We used microarray data from UK Biobank to investigate the preva

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

143 Microarray data gained with these DCs showed a significa

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

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

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

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

148 near models to existing liver RNA expression microarray data (GSE9588) and RNA-seq data from genotype

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

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

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

152 Custom microarray data have been generated using RNA isolated f

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

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

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

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

157 The microarray data identified 454 candidate genes with expr

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

159 In a subpopulation with microarray data, IgE to the major timothy grass allergen

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

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

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

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

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

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

166 Biolog Phenotype MicroArray data indicated that mmp deletion increased su

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

168 We employed microarray data integration to compare the molecular pat

169 ampsia and endometrial disorders revealed by microarray data integration.

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

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

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

173 Removing systematic noise from microarray data is therefore crucial.

174 Traditional glycan microarray data is typically presented as excel files wi

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

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

177 control groups, using the linear models for microarray data (linear modeling) and Boruta (decision t

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

179 co-expression analysis of publicly available microarray data (n = 303 profiles) measured in livers of

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

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

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

183 ost gene expression signatures obtained from microarray data of B. pseudomallei-infected cases to dev

184 Microarray data of chorionic villous samples (CVSs) obta

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

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

187 son of this data set with publicly available microarray data of PPK lesions from individuals with PC

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

189 Using available microarray data on 411 muscle samples from patients with

190 Analysis of microarray data on gene expression and methylation allow

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

192 mRNA transcriptome data from newly generated microarray data on IHs with publicly available data on t

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

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

195 2 pathways by RNA-Seq data only, and none by microarray data only.

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

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

198 PARZ trial, 375 (83%) patients had available microarray data, pretreatment BMI measurements, and over

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

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

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

202 Our microarray data revealed a distinct gene expression prof

203 ion of linc-SPRY3-2/3/4 in NSCLC RNA-seq and microarray data revealed a negative correlation between

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 Mining public microarray data revealed that NCOR1-targeted genes were

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

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

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

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

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

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

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

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

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

218 licly available glioblastoma gene expression microarray data set.

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

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

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

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

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

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

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

226 GA stress using existing RNA sequencing and microarray data sets generated using human islets from d

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

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

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

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

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

232 We analyzed publicly available microarray data sets to identify dysregulated lncRNAs in

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

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

235 By analyzing two publicly available microarray data sets, we found that NPFF is consistently

236 naling molecular targets by meta-analysis of microarray data sets.

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

238 munofluorescence, and by analyzing published microarray data sets.

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

240 Microarray data showed that 324 genes were up-regulated

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

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

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

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

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

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

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

248 comparative analysis between proteomics and microarray data, significantly higher degrees of correla

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

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

251 Microarray data suffers from several normalization and s

252 Microarray data suggest that SDSE degraded host tissue p

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

254 ome analysis coupled with publicly available microarray data suggested a mechanism of impaired PLGA d

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

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

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

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

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

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

261 In this study, we used cDNA microarray data to determine APLNR expression levels in

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

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

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

265 The unique feature of GlyMDB is to link microarray data to PDB structures.

266 GLAD extends the capability of the microarray data to produce more comparative visualizatio

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

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

269 We preprocessed the microarray data using the Robust Multichip Average (RMA)

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

271 Microarray data, validated against direct RNA sequencing

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

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

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

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

276 Using data mining techniques on existing microarray data, we found that mRNA expression of the CS

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

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

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

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

281 cDNA microarray data were collected both from patients enroll

282 Microarray data were further validated by immunoblotting

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

284 RNAseq and microarray data were obtained for 1032 gliomas from the

285 The microarray data were then used to design a biomarker pan

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

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

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

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

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

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

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

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

294 sed search is performed using BLAST to match microarray data with all available PDB structures contai

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.