ライフサイエンスコーパス: expression analysis

1 42, and 84 and processed for microarray gene expression analysis.

2 ) CD14(+) -macrophages was subjected to gene expression analysis.

3 (PCA) indicated high quality of differential expression analysis.

4 ermoneutral and heat stress periods for gene expression analysis.

5 es and perform downstream cell type-specific expression analysis.

6 th mixed effects are needed for differential expression analysis.

7 tool to perform integrative 5mCG, 5hmCG and expression analysis.

8 types based on immunohistochemistry and gene expression analysis.

9 nctional module-induced regulation of target expression analysis.

10 uld be an integral component of differential expression analysis.

11 or binding sites (TFBSs) is crucial for gene expression analysis.

12 the first two is sufficient for robust gene expression analysis.

13 d the three kits in a realistic differential expression analysis.

14 confounding behavior regarding differential expression analysis.

15 l design in the context of differential gene expression analysis.

16 as analyzed through histological and protein expression analysis.

17 ripts were identified via a pathway level co-expression analysis.

18 ed by histology, western immunoblot and gene expression analysis.

19 -seq) revolutionized cell type-specific gene expression analysis.

20 transcriptome evidences from region-specific expression analysis.

21 gle-neuron calcium dynamics followed by gene expression analysis.

22 was quantified by Picro Sirius Red and gene expression analysis.

23 roving both clustering and gene differential expression analysis.

24 , cell type annotation and differential gene expression analysis.

25 classification, regression, and differential expression analysis.

26 tistically associated with UC based on GEO2R expression analysis.

27 he stage of development as revealed by c-Fos expression analysis.

28 tial expression of individual genes or on co-expression analysis.

29 seq) in combination with single-cell protein expression analysis.

30 ury was evaluated by histopathology and gene expression analysis.

31 ples taken for immunohistochemistry and gene expression analysis.

32 ell as the effect on subsequent differential expression analysis.

33 f quantification and downstream differential expression analysis.

34 We show that for conventional expression analysis, a size range between 22 and 30 nucl

35 and specificity of single-cell differential expression analysis: a large proportion of expressed gen

36 eds to be considered, requiring differential expression analysis across samples.

37 nd noncoding genes, (b) perform differential expression analysis across thirteen cancer types, identi

38 Gene expression analysis (Affymetrix) was performed in MM-MSC

39 Gene expression analysis after coculture revealed simultaneou

40 We propose a new generalized gene co-expression analysis algorithm via subspace clustering th

41 Our discrete, differential expression analysis also identified SUZ12 and FOXA1 as p

42 Co-expression analysis also revealed that the most downregu

43 h of gene silencing, metabolomics, real time expression analysis and ab initio bioinformatics tools l

44 Here, we perform gene expression analysis and ChIP followed by sequencing (ChI

45 Gene expression analysis and chromatin immunoprecipitation se

46 include co-expression analysis, differential expression analysis and differential correlation analysi

47 mRNA expression analysis and differential proteomics on betaF

48 for mesoderm specification as shown by gene expression analysis and histochemical staining for cardi

49 In vivo proinflammatory cytokine expression analysis and histopathological analysis of th

50 this study, we performed a differential gene expression analysis and identified a gene, Regucalcin (R

51 nced speckle tracking echocardiography, gene expression analysis and immunohistological staining.

52 mbining adipose transcriptome datasets in co-expression analysis and in differential expression analy

53 with fractions subsequently amenable to gene expression analysis and in vitro cell culture.

54 Our detailed expression analysis and inhibitor studies suggest that R

55 Proteomics is a powerful tool for protein expression analysis and is becoming more readily availab

56 Molecular analysis techniques such as gene expression analysis and proteomics have contributed grea

57 rative phylogenetic studies, high-throughput expression analysis and quantitative RT-PCR analysis was

58 Global expression analysis and real-time PCR assays revealed th

59 ves accuracy and sensitivity of differential expression analysis and reduces batch effect compared wi

60 Bulk expression analysis and single molecule RNA-fluorescence

61 Bulk tumor gene expression analysis and single-cell RNA sequencing demon

62 Using differential gene expression analysis and weighted gene co-expression netw

63 ular endomyocardial biopsies (n=12) for mRNA expression analysis, and compared baseline transcript le

64 erformed chromatin immunoprecipitation, gene expression analysis, and enhanced reduced representation

65 vances in transcriptomics, multiplex protein expression analysis, and experimental depletion of micro

66 alyzed human databases, undertook gene array expression analysis, and generated unique murine models

67 We used multiparametric flow cytometry, gene expression analysis, and phagocytosis/transferrin uptake

68 is, imaging analysis, machine learning, gene expression analysis, and sequence analysis.

69 Using differential gene expression analysis as an example, we showed that when m

70 Gene expression analysis at the point-of-care is important fo

71 comparative transcriptome profiling and gene expression analysis between a nearly-isogenic Sw-7 line

72 edicle, we performed an RNA-seq differential expression analysis between cone-specific Bmal1 knockout

73 Differential expression analysis between optic fissure and dorsal ret

74 We performed targeted gene deletion, expression analysis, biochemistry and pathogenicity assa

75 Morphometric assessments, expression analysis, blood pressure measurements, and si

76 Differential gene expression analysis by RNA sequencing of F4/80(+) phagoc

77 acrophage polarization was confirmed by gene expression analysis by significant mRNA downregulation o

78 ; and POSTN, IL33, TPSAB, TPSB, and CMA gene expression analysis by using quantitative RT-PCR.

79 We used differential expression analysis combined with weighted gene coexpres

80 uncatula SUPERMAN (MtSUP) gene based on gene expression analysis, complementation and overexpression

81 nCounter expression analysis confirmed the array results (P < 0.0

82 RNA-Seq expression analysis currently relies primarily upon exon

83 ounding effects of cell type in differential expression analysis (DEA).

84 Gene expression analysis demonstrated the transcription of si

85 Statistical analyses include co-expression analysis, differential expression analysis an

86 However, differential expression analysis does not provide quantitative predic

87 applications to gene discovery, differential expression analysis, eQTL prioritization, and pathway en

88 re are many methods for RNA-seq differential expression analysis, existing methods do not properly ac

89 Using allele-specific expression analysis, expressed fs-indels are enriched in

90 ost popular methods for RNA-seq differential expression analysis fit a parametric model to the counts

91 brain regions, age ranges and sexes; (ii) co-expression analysis from different platforms.

92 ules, we also extract nucleic acids for gene expression analysis from living cells without affecting

93 simultaneous normalization and differential expression analysis from log-transformed RNA-seq data.

94 Using differential expression analysis, gene set enrichment analysis, and e

95 Differential expression analysis generated gene lists that identify t

96 abundance estimation (RSEM) and differential expression analysis, genes that were significantly diffe

97 Previous genome-wide DNA-binding and expression analysis has identified a set of genes that a

98 Co-expression analysis has provided insight into gene funct

99 Global gene-expression analysis has shown remarkable difference betw

100 Studies using expression analysis have indicated that MELK expression

101 Co-expression analysis highlights the function of mitochond

102 Differential expression analysis identified 143 genes significantly a

103 Differential expression analysis identified 542 genes enriched in syn

104 Differential expression analysis identified 699 significantly regulat

105 Single-cell gene expression analysis identified a discrete ILC2-committed

106 Detailed gene expression analysis identified a unique pattern of cellu

107 Differential expression analysis identified cell type-specific genes

108 Differential gene-expression analysis identified major gene-expression cha

109 Gene expression analysis identified transcription factors dif

110 Differential expression analysis identifies global changes in transcr

111 Differential gene expression analysis in 22Rv1 cells confirmed that PRMT5

112 ion heritability estimation and differential expression analysis in a large RNA sequencing study in t

113 Global gene expression analysis in AD-HIES patient skin fibroblasts

114 Gene expression analysis in adipose tissue suggested that DNT

115 Gene expression analysis in cancer patient datasets indicated

116 Here, high-throughput gene expression analysis in CD4+ T cells showed that the top

117 Gene co-expression analysis in cells robustly expressing SLC12A2

118 These findings demonstrate that gene-expression analysis in isolation is insufficient to iden

119 Differential gene expression analysis in mature and immature ovaries ident

120 ted proteins at cell junctions, and for gene expression analysis in multiple individual neurons of th

121 We used gene-expression analysis in quiescent cells to analyze respon

122 Specifically, we perform differential expression analysis in situations where samples cannot b

123 group also discussed the role of tissue gene expression analysis in the context of unmet needs in lun

124 Finally, gene expression analysis in the medial prefrontal cortex (mPF

125 HASE gene RhPAAS An in-depth allele-specific expression analysis in the progeny demonstrated that onl

126 In this study, we used genome-wide expression analysis in U87 GBM to identify NF-kappaB-dep

127 AHA2 protein biochemistry and functional expression analysis in Xenopus oocytes indicates that th

128 On the basis of allelic expression analysis, in addition to the conserved genes,

129 Gene expression analysis indicated VCP expression was particu

130 Large-scale gene expression analysis is a valuable asset for data-driven

131 uencing experiments followed by differential expression analysis is a widely used approach for detect

132 Gene expression analysis is emerging as a new diagnostic tool

133 the role of miRNAs in CRC, an in-depth miRNA expression analysis is essential to identify clinically

134 A major challenge in gene expression analysis is to accurately infer relevant biol

135 An important challenge in gene expression analysis is to improve hub gene selection to

136 A fundamental step in differential expression analysis is to model the association between

137 Gene expression analysis may simultaneously quantify numbers

138 oles in the performance of differential gene expression analysis methods and need to be considered in

139 compare the performance of differential gene expression analysis methods for scRNAseq data.

140 our method, Swish, with popular differential expression analysis methods.

141 Moreover, gene expression analysis not only revealed correlated express

142 face proteomics with in-depth, unbiased gene expression analysis of > 6400 single cells ex vivo from

143 Using an unbiased expression analysis of 34 putative autophagy genes acros

144 NA-sequencing of 28 tumors, bulk genetic and expression analysis of 401 specimens from the The Cancer

145 In a differential-expression analysis of a real single-cell RNA-seq datase

146 ere, we describe the identification and mRNA expression analysis of duck IL-23p19 (duIL-23p19) in spl

147 In addition, gene expression analysis of F1 hybrid mice from CAST x FVB re

148 Expression analysis of FAE1, AtDGAT1, AtLPCAT1 and AtPDA

149 The expression analysis of Fie1, a rice FERTILIZATION-INDEPE

150 Gene expression analysis of gene expression revealed that dow

151 Gene expression analysis of gene modules related to the affec

152 Moreover, expression analysis of genes associated with inflammatio

153 In Cynops, the expression analysis of genes described to be sex-related

154 e performed a genome-wide identification and expression analysis of genes encoding ANK proteins in th

155 We also show that differential expression analysis of genes with biased expression esti

156 mitochondrial activity in vivo Finally, gene expression analysis of liver samples from obese patients

157 e we perform whole exome sequencing and gene expression analysis of matched primary breast tumours an

158 comprehensive condition and tissue-specific expression analysis of metabolic gene clusters, we devel

159 High-throughput gene-expression analysis of microglial-enriched genes involve

160 Gene expression analysis of mirn23a-deficient myeloid progeni

161 Differential expression analysis of mRNA from chondrocytes harvested

162 Differential expression analysis of mRNA-seq data revealed that Nsp1

163 Gene-expression analysis of MYC-driven medulloblastoma cells

164 teria and paired host-pathogen temporal gene expression analysis of Mycobacterium tuberculosis infect

165 Expression analysis of nine selected candidate genes in

166 me eggs was monitored in real time, and gene expression analysis of oncogenesis, epithelial to mesenc

167 Expression analysis of PHYTOENE SYNTHASE (PSY1) and CARO

168 Protein expression analysis of PP2A family members revealed that

169 muscle, adipose tissue, and liver alongside expression analysis of proteins implicated in insulin ac

170 tical EcoToxModules were identified using co-expression analysis of publicly available microarray dat

171 n to enable the isolation, tracking and gene expression analysis of rare cells.

172 nivariate methods developed for differential expression analysis of RNA-seq data.

173 q shares many similarities with differential expression analysis of RNA-seq data.

174 ental approach that involved the global gene expression analysis of strains D23580 and 4/74 grown in

175 We conducted high-throughput gene expression analysis of Th17-enriched CCR6(+)CXCR3(-)CD45

176 Gene expression analysis of the ACT products indicated that a

177 Differential expression analysis of the alternatively spliced genes i

178 e-d-4-ol were detectable in planta, and gene expression analysis of the biosynthetic TPSs showed dist

179 Expression analysis of the corresponding genes may sugge

180 Whole genomic expression analysis of the E2/E4/E5 pharyngeal cancer su

181 Through expression analysis of the gene signatures of cardiac fi

182 Transcriptome and gene-specific expression analysis of the hippocampus showed dysregulat

183 Expression analysis of the introgressed region that is s

184 Tissue expression analysis of the SNP-level data found enrichme

185 Expression analysis of the target genes, Hex-1 and Cell-

186 Weighted gene co-expression analysis of the transcriptome of filamentous

187 sequenced and quantified, and a differential expression analysis of the two RNA populations performed

188 We performed proteomic and gene expression analysis of these cells before and after rece

189 Expression analysis of these factors revealed downregula

190 Here, we performed genome-wide expression analysis of Toll-like receptor (TLR)-stimulat

191 ing theses three modifications unbiased gene expression analysis on human salivary RNA can be perform

192 a Bayesian model for single cell transcript expression analysis on MERFISH data.

193 this study, we performed joint differential expression analysis on the RNA-sequencing data from both

194 similar function, based on differential gene expression analysis or co-expression network analysis.

195 ses, including quality control, differential expression analysis, pathway enrichment analysis, differ

196 We present a gene expression analysis platform using a giant magnetoresist

197 a computational approach that combines gene expression analysis, previous knowledge from proteomic p

198 While global gene expression analysis profiles the average expression of a

199 or related species followed by differential expression analysis, quantitative PCR validation and det

200 A differential expression analysis restricted to the 40 to 60 year age

201 Genome-wide RNA-seq and differential expression analysis reveal differences in adipocyte and

202 Gene expression analysis revealed a 4-fold downregulation of

203 Frizzled 1-10 expression analysis revealed a distinct expression patte

204 Gene expression analysis revealed a proliferative phenotype w

205 Comprehensive gene expression analysis revealed a sustained disruption of p

206 Mechanistically, gene expression analysis revealed betaglycan controls the exp

207 Gene expression analysis revealed elevated levels of angiogen

208 Interestingly, single-cell differential expression analysis revealed GABA treatment gave rise to

209 Differential gene expression analysis revealed Geobacter's transcriptional

210 Immunophenotyping and CD4 T-cell ISG expression analysis revealed marginal differences across

211 Further, comparative gene expression analysis revealed several genes, which displa

212 Single-cell gene expression analysis revealed significant co-expression o

213 Detailed gene expression analysis revealed that ATF3 is one of the mos

214 Unexpectedly, expression analysis revealed that FGF15 is generated by

215 Gene expression analysis revealed that Id2 expression was ess

216 A more detailed mitochondrial gene expression analysis revealed that in particular mitochon

217 Gene expression analysis revealed that in Th17 cells, VHL reg

218 Gene expression analysis revealed that mouse tumors exhibited

219 Diurnal expression analysis revealed that only 13% of Wolffia ge

220 Expression analysis revealed that Regnase-3 and Regnase-

221 However, gene expression analysis revealed that RIPK3 is abundantly ex

222 Gene expression analysis revealed that Tfh cells induce Myc e

223 Differential gene expression analysis revealed that the down-regulated gen

224 Gene expression analysis revealed that the expression of SUPP

225 Differential gene expression analysis revealed that the extent of differen

226 Gene expression analysis revealed that the MB-positive cardio

227 Gene expression analysis revealed the loss of gene changes as

228 Differential expression analysis revealed, 160 transcripts, out of th

229 These data integrated with gene expression analysis, revealing increased expression of t

230 n a western species, Bombus melanopygus Gene expression analysis reveals distinct shifts in Abd-B ali

231 Gene expression analysis reveals enrichment, but not cosegreg

232 Differential gene expression analysis reveals limited overall changes in R

233 Gene expression analysis reveals that messenger RNAs encoding

234 In addition, we combined RNA expression analysis (RNA sequencing) with the ChIP-seq r

235 ing, gene deletion and complementation, gene expression analysis, sequence divergence, defoliating ph

236 ing a cost-effective approach for gene-level expression analysis should prefer short paired-end reads

237 Tregs gene expression analysis showed a differential signature betw

238 Gene expression analysis showed a marked up-regulation of con

239 Human gene expression analysis showed increased expression of infla

240 Gene expression analysis showed that both CER1 and CER1-LIKE1

241 In addition, CESA expression analysis showed that diurnal expression patte

242 The gene expression analysis showed that diverse chromatin states

243 Protein expression analysis showed that ENO1 is expressed in pDC

244 Gene co-expression analysis showed that in addition to overexpre

245 Human ocular tissue gene expression analysis showed that most of the identified g

246 Differential expression analysis showed that most of the identified m

247 Furthermore, gene expression analysis showed that patients with pancreatic

248 Gene expression analysis showed that the cranial subpopulatio

249 Expression analysis showed that twelve of the lettuce CB

250 Finally, tissue-specific expression analysis shows that modification genes are hi

251 the performance of eleven differential gene expression analysis software tools, which are designed f

252 teraction networks were derived from protein expression analysis software, and cellular function acti

253 can alter the conclusions of a differential expression analysis study.

254 Gene expression analysis suggested that PXY and ER cross- and

255 The clustering and co-expression analysis suggested that transcription factor

256 Here, we performed a gene expression analysis targeting components of the DNA repa

257 A differential expression analysis technology developed for linear mode

258 and deliver stable cDNA for downstream gene expression analysis, thereby allowing chip-based integra

259 matical modeling with quantitative trait and expression analysis to build a model that describes how

260 B-cell lymphoma can be identified using gene-expression analysis to determine their cell of origin, c

261 le-cell RNA sequencing with comparative gene expression analysis to elucidate the transcriptional dyn

262 We used single cell gene expression analysis to evaluate antigen-specific memory

263 (2020) performed high-resolution gene expression analysis to identify significant similarities

264 assays, genome resequencing, and global gene expression analysis to study the effect of transmission

265 By extending classic allele-specific expression analysis to the allopolyploid level, we disti

266 Here, we present a differential expression analysis toolkit, DEvis, that provides a powe

267 Notably, genome-wide expression analysis uncovered a melanocytic plasticity s

268 Gene expression analysis under glyphosate stress showed trans

269 st studies of computational methods for gene expression analysis use simulated data to evaluate the a

270 Expression analysis using a lacZ reporter and single-cel

271 re performed a genome-wide differential gene expression analysis using RNA sequencing (RNA-seq) on pr

272 Additionally, a gene expression analysis using the National Cancer Institute

273 Expression analysis using whole eye mRNA revealed the dy

274 Subsequent gene/ncRNA expression analysis was assessed using quantitative reve

275 A large-panel gene expression analysis was conducted to identify biomarkers

276 Differential gene expression analysis was performed across the development

277 CPAMD8 RNA expression analysis was performed on tissues dissected f

278 Differential gene expression analysis was performed to evaluate for host t

279 ormed to assign samples to clusters and gene expression analysis was performed to identify differenti

280 ovine reference genome and differential gene expression analysis was performed using EdgeR.

281 In prior studies, gene expression analysis was shown to stratify patient outcom

282 Using an OSN-specific gene expression analysis, we explore downstream targets of in

283 Through whole-genome expression analysis, we found that an HIF-1alpha transcr

284 Through gene expression analysis, we found that treatment of CD4(+) T

285 Applying differential expression analysis, we highlighted genes and transcript

286 Through differential gene expression analysis, we identified SPRY4 as the potentia

287 By gene expression analysis, we identified two molecules that co

288 Through a combination of proteomics and gene expression analysis, we identify enzymes involved in car

289 ed splenic T cells, flow cytometry, and gene expression analysis, we observed here that higher Trx ex

290 Through metabolic gene expression analysis, we pinpoint the loss of squalene mo

291 By differential allelic gene expression analysis, we showed in our samples and a popu

292 tterns, functionality, and gene and microRNA expression analysis were further investigated in 10 cSCC

293 age, whole-lung cellular isolation, and gene expression analysis were performed on 3-wk- (juvenile) a

294 aflatoxin B(1) (AFB(1)) production and gene expression analysis, were carried out to provide insight

295 l increases the capabilities of differential expression analysis while reducing workload and the pote

296 We combine this live-cell gene expression analysis with detailed physiologic phenotypin

297 tify the features in downstream differential expression analysis with high accuracy when applied to t

298 hat uniquely combines discrete, differential expression analysis with in silico differential equation

299 n co-expression analysis and in differential expression analysis with obesity-related traits.

300 ng alternative splicing or differential gene expression analysis, without including a non-essential t