ライフサイエンスコーパス: expression data

1 g Indications Using Compendia of Public Gene Expression Data".

2 cally meaningful abstractions of cancer gene expression data.

3 across different individuals, based on gene expression data.

4 ve for classification than the original gene expression data.

5 cal clusters of co-regulated genes from gene expression data.

6 confirmed the candidate effectors by gene co-expression data.

7 does not adequately explain our quantitative expression data.

8 o computationally infer TRN from time series expression data.

9 archy of these networks can be inferred from expression data.

10 dimension representation of the single cell expression data.

11 ative mammals and birds with substantial new expression data.

12 To date, these models have utilized only expression data.

13 el captured intermediate and phenotypic gene expression data.

14 proach uses signatures extracted from public expression data.

15 ensemble framework using yeast PINs and gene expression data.

16 east and lung cancer cells, and in deposited expression data.

17 n C. elegans based on new and published gene expression data.

18 ithms in both bulk and single-cell synthetic expression data.

19 al insight from complex and often noisy gene expression data.

20 ethodology by analyzing uterine fibroid gene expression data.

21 lineage relationships from single-cell gene expression data.

22 ate assessment of protein activity from gene expression data.

23 alterations (CNAs), DNA methylation, and RNA expression data.

24 a in over 1000 cancer cell lines and protein expression data.

25 nes in a given tissue using time-course gene expression data.

26 ncer cell lines, consistent with the in vivo expression data.

27 To this end we reanalyzed public expression data.

28 Omnibus (GEO) is a public repository of gene expression data.

29 models (DTM) for analyzing time-series gene expression data.

30 thod for biomarker identification using gene expression data.

31 cance measure in the analysis of genome-wide expression data.

32 ns that were not readily observed in the RNA expression data.

33 r in a collection of 4,801 breast tumor gene expression data.

34 r and immune cell types from bulk tumor gene expression data.

35 on should be applied when interpreting tumor expression data.

36 ommunication of experimental methodology and expression data.

37 ene regulatory network from single-cell gene expression data.

38 he hierarchical structure within cancer gene expression data.

39 ation coefficient (PCC) calculated from gene expression data.

40 genes from GWAS summary statistics and gene expression data.

41 ting one use of these cell-specific microRNA expression data.

42 ranscription factor just using only the gene expression data.

43 lied to genome-wide DNA methylation and gene expression data across 763 primary samples identifies ve

44 on threshold expression values, and compare expression data across different developmental stages, t

45 ta-analysis methods designed to combine gene expression data across many tissues to increase power fo

46 control is an important step for integrating expression data across platforms.

47 Gene expression data analysis also suggests that the noncanon

48 Based on gene expression data analysis, we found that MSI2 expression

49 REAM complex binding data, p53-depedent mRNA expression data and a genome-wide definition of phylogen

50 archical model integrating genetics and gene expression data and combining this with survival analysi

51 CellNet takes as input gene expression data and compares them with large data sets o

52 e integration of multiple types of both gene expression data and database schema.

53 struction of co-expression network from gene expression data and for extraction of densely connected

54 d to visualize the spatiotemporal context of expression data and help elucidate gene function.

55 hancer hijacking) by integrating SCNAs, gene expression data and information on topologically associa

56 ort for new data types such as CRAM, RNA-seq expression data and long-range chromatin interaction pai

57 ioinformatics modeling analyzed whole-genome expression data and matched PPC chemotactic cell-surface

58 gical differences between samples using gene expression data and pre-defined biological pathway infor

59 transcriptional activity inferred from mRNA expression data and protein reporter data in the core ci

60 However, high noise levels in gene expression data and relatively high correlation between

61 RNs using in silico time-stamped single cell expression data and single cell transcriptional profiles

62 gical issues related to the analysis of gene expression data and social gradients in health and a nee

63 ogicTRN can naturally integrate dynamic gene expression data and TF-DNA-binding signals in order to i

64 of simulated cells to recreate experimental expression data and the effects of noise on the dynamics

65 connection between the representation of the expression data and the number of cell types that can be

66 We apply BicMix to breast cancer gene expression data and to gene expression data from a cardi

67 scription factors when benchmarked with mRNA expression data and transgenic reporter assays.

68 ly publish results without publishing source/expression data, and get all the functionality of a web

69 l as the ability to process quantitative RNA expression data, and heterogeneous combinations of RNA a

70 te cell fraction predictions from tumor gene expression data, and provides a unique novel experimenta

71 r each species by a cluster analysis of gene expression data, and subsequently computes the overlaps

72 ls in molecular networks built with our gene expression data, and we confirmed upregulation of MMP9 a

73 e skin in publicly available genotype-tissue expression data, and we generated preliminary evidence f

74 Overall, public gene expression data are a useful tool for benchmarking gene

75 Gene expression data are accumulating exponentially in public

76 The expression data are combined with genetic and phenotypic

77 independently derived from eQTL and allelic expression data are highly consistent, and identify tech

78 Studies that combine genomic and gene expression data are scarce, however, particularly in inv

79 or making quality gene signatures using gene expression data as initial inputs.

80 on designed for exploratory analysis of gene expression data, as well as data from related experiment

81 However, if the gene expression data available are only from the mature cells

82 ynamic information from single-cell snapshot expression data based on expression profiles of 48 genes

83 d feature selection method for temporal gene expression data based on maximum relevance and minimum r

84 However, studies directly comparing expression data between nuclei and whole cells are lacki

85 Combining the expression data, biochemical properties, and cellular fe

86 gical differences between samples using gene expression data by assuming that ontologically defined b

87 composition of a solid tumour from bulk gene expression data by mathematical deconvolution, using ind

88 We show that gene expression data can be computationally constructed, ther

89 Genome-wide miRNA expression data can be used to study miRNA dysregulation

90 Gene expression data can help researchers understand the dive

91 We used gene expression data collected from whole blood from 862 indi

92 We focused on time-stamped cross-sectional expression data, commonly generated from transcriptional

93 It contains over 3144 microarrays of gene expression data corresponding to rat livers treated with

94 DASHR annotation and expression data covers all major classes of sncRNAs incl

95 omparisons with primary mouse and human gene expression data demonstrated rostral and caudal progenit

96 We next analyze mRNA and miRNA cancer expression data, demonstrating the advantage of using th

97 rent options to mine publicly available gene expression data deposited in NCBI's gene expression omni

98 e bioinformatics analysis of microarray gene expression data derived from a set of high-grade human g

99 in 1,170 adults with asthma, each with gene expression data derived from either whole blood (WB) or

100 reast tumors and time-course microarray gene expression data derived from in-vivo muscle regeneration

101 Paired microarray gene expression data derived from peripheral blood mononuclea

102 ation of tumor surrounding normal cells, the expression data derived from tumor samples would always

103 rmacological data, combined with our protein expression data, did not allow us to pinpoint one PGE2-G

104 GOexpress integrates normalised gene expression data (e.g., from microarray and RNA-seq exper

105 ering of transcriptional networks using gene expression data enables identification of genes that und

106 ide regulatory landscapes from temporal gene expression data, especially for complex eukaryotes like

107 sted this by carrying out a meta-analysis of expression data for 1,235 NLRs from nine plant species.

108 igned to work with human or mouse microarray expression data for 21 cell or tissue (C/T) types.

109 We examined clinical, genetic and expression data for 284 individuals with psychosis deriv

110 ng ChIP-seq data on 113 TFs and matched gene expression data for 3863 putative target genes.

111 species analyses now enable users to analyse expression data for all species simultaneously, and iden

112 Microarray expression data for genes preferentially expressed in hu

113 By analyzing The Cancer Genome Atlas mRNA expression data for HGS ovarian cancer patient samples,

114 o genome assembly and genome-wide transcript expression data for Kalanchoe fedtschenkoi, an obligate

115 ric Genomics Consortium, along with the gene expression data for multiple tissues from the Genotype-T

116 oci (cQTLs) using parallel genotype and gene expression data for segregants from a cross between two

117 ed in exploring the prognostic value of gene expression data for specific subtypes or stages of a dis

118 ct was assigned to endotype A or B using the expression data for the 100 endotyping genes.

119 Analyzing genotype, phenotype and expression data from 20 pedigrees, the members of our Ge

120 The CKDdb database contains differential expression data from 49395 molecule entries (redundant),

121 equencing experiments with genotype and gene expression data from 602 prostate tumor samples.

122 east cancer gene expression data and to gene expression data from a cardiovascular study cohort, and

123 d target gene list and combines it with gene expression data from a context, quantifying that regulat

124 that provides an interface to simulate gene expression data from a given gene network using the stoc

125 TPS utilizes expression data from a small set of genes sampled at a h

126 We illustrate the method using gene expression data from a study of haematopoiesis.

127 rithm in practice using the time-course gene expression data from a study on human respiratory epithe

128 we show, using longitudinal whole-blood gene expression data from a twin cohort, that the genetic arc

129 latform will facilitate the analysis of gene expression data from a wide variety of species by enabli

130 Analyses of global gene expression data from adipose tissue, skeletal muscle, an

131 Gene expression data from an open public data set was also an

132 implementation for generating realistic gene expression data from biologically relevant networks that

133 We applied our approaches to expression data from blood and adipose tissue measured i

134 Expression data from both wild-type and mutant mice are

135 ng the large collection of drug-induced gene expression data from Connectivity Map, several drugs wer

136 the projection of baseline and differential expression data from curated expression studies in plant

137 east and colorectal cancers) by merging gene expression data from different studies after diagnosing

138 n studies (GWAS) and massive amounts of gene expression data from different tissues.

139 s atlas can be adapted to different types of expression data from diverse multicellular species.

140 RolyPoly is designed to use expression data from either bulk tissue or single-cell R

141 Furthermore, gene-expression data from feathers of different bird species

142 d and trained with perturbation-derived gene expression data from five hepatocyte donors.

143 We generated gene expression data from fluorescence-activated cell sorted

144 Gene sequence and expression data from four major organs of A. amnicola pr

145 Using gene expression data from GBM stem-like cells, astrocytes, an

146 cell (PBMC) microRNA and protein-coding gene expression data from healthy controls and patients with

147 tagging by obtaining cell type-specific gene expression data from intact Drosophila larvae, including

148 cus on the problem of finding eGenes in gene expression data from many tissues, and show that our mod

149 Using gene expression data from mice and humans with mitochondrial

150 ene-based expression overviews; and links to expression data from other species.

151 l DNA methylation, gene expression and miRNA expression data from ovarian cancer samples obtained fro

152 Applying this framework to tumor expression data from patients, we stratified tumors into

153 We performed a meta-analysis of gene expression data from skin biopsies of patients with syst

154 hypothesis was developed on the basis of RNA expression data from snapshot and/or population-averaged

155 Our gene signature was based on gene expression data from Taiwan female non-smoker lung cance

156 By analyzing post-mortem gene expression data from the Allen Brain Atlas, we investiga

157 tion, an unbiased clustering method, on mRNA expression data from the cancer genome atlas (TCGA) and

158 Clinical samples and gene expression data from The Cancer Genome Atlas (TCGA) demo

159 ted GEMINI on breast and ovarian cancer gene expression data from The Cancer Genome Atlas project and

160 We analyzed clinical and gene expression data from The Cancer Genome Atlas, Oncomine,

161 ues using both noisy simulated data and gene expression data from The Cancer Genome Atlas.

162 The multitissue expression data from the Genotype-Tissue Expression (GTE

163 LDGM by applying to the brain and blood gene expression data from the GTEx consortium.

164 scriptomics (Nephroseq) and relevant protein expression data from the literature.

165 conducted a study of publicly available gene expression data from three cohorts of ACC patients to id

166 ng new data types (including diversity data, expression data, gene models, and metabolic pathways), a

167 z-scores), it is generally applicable to the expression data generated by either microarray or RNA-se

168 sed investigation of the large-scale protein expression data generated by this platform, we have deve

169 cell (LSC) gene expression profiles and gene expression data generated from JAM-C-expressing leukemic

170 Reconstructions of gene networks from gene expression data greatly facilitate our understanding of

171 discriminant genes and synergic genes, from expression data has been useful for medical diagnosis an

172 d fundamental biological processes from gene expression data has grown in parallel with the recent ex

173 Comparative analysis of cancer cell expression data highlights many deregulated miRNAs.

174 Analysis of RNA expression data identified a 38-transcript signature dis

175 ng quantitative CK5 and BCL6 mRNA or protein expression data identified patients at high or low risk

176 f chromatin accessibility profiles with gene expression data identified unique regulatory modules tha

177 Gene expression data imply that under low pH conditions, both

178 With expression data imported into PhenomeScape, the user can

179 it is becoming more feasible to obtain gene expression data in addition to genotypes and phenotypes,

180 y demonstrates that even a small set of gene expression data in addition to sequence homologies are i

181 within the existing vast collection of gene expression data in Arabidopsis.

182 We also reanalyzed miRNA expression data in Chlamydomonas subject to sulfur or ph

183 ered by using The Cancer Genome Atlas (TCGA) expression data in CRC, whereby we generated a core regu

184 laim, we performed a statistical analysis of expression data in different transform domains and devel

185 generated a uniquely large resource of gene expression data in four interconnected limbic brain regi

186 of genome-wide methylation, gene and protein expression data in human primary chondrocytes, we identi

187 a powerful approach to analyze and interpret expression data in microarray and shotgun proteomics.

188 alysis can be applied to genome-wide SNP and expression data in order to identify transcripts that ar

189 for analyzing omics data in general and gene expression data in particular.

190 The abundance of spatial expression data in recent years has led to the modeling

191 However, the scarcity of gene expression data in reptiles, crucial for understanding e

192 ystematic search for publicly available gene expression data in sepsis and tested each gene expressio

193 y recently developed, the ability to profile expression data in single cells (scRNA-Seq) has already

194 ines and applying these models to tumor gene expression data in the clinical data sets (e.g., TCGA).

195 those that induce IL-13 production, and gene expression data indicate that an alternative activation

196 findings suggest promise in integrating gene expression data into population studies and provide furt

197 is ineffective to deal with the noise in the expression data introduced by the complicated procedures

198 stance based unsupervised clustering of gene expression data is commonly used to identify heterogenei

199 The analysis of epigenetic and expression data is therefore incomplete if RNA-based reg

200 In the analysis of large-scale gene expression data, it is important to identify groups of g

201 However, protein expression data linked to high-quality DNA, RNA, and dru

202 bout the identification of modules from gene expression data mapped on protein interaction networks,

203 yer of a deep learning model trained on gene expression data may represent signals related to transcr

204 cs-the integration of GWAS signals with gene expression data-may illuminate genes and gene networks t

205 We apply our method to empirical gene expression data measured in 373 European individuals fro

206 notypic traits of interest, we analyzed gene expression data, metabolite data obtained with GC-MS and

207 that consisted of clinical and messenger RNA expression data (n = 166).

208 of 127 TFs through analysis of RNA-seq gene expression data newly generated for 448 cancer cell line

209 plant materials RNA-Seq data, parameters for expression data normalization and network inference were

210 h as microbial community composition or gene expression data, observations can be generated from a co

211 We analyzed gene expression data obtained from the Lung Tissue Research C

212 ine Encyclopedia, which provides genetic and expression data of 496 cell lines together with their re

213 ering approach which first denoises the gene expression data of each species into a data matrix.

214 e NanoString platform, using microarray gene expression data of matched fresh frozen biopsies as a go

215 es is demonstrated on numerically mixed gene expression data of ovarian cancer subtypes.

216 An application to the analysis of gene expression data of patients with bladder cancer is final

217 an application of SINCERITIES to single cell expression data of T2EC chicken erythrocytes pointed to

218 datasets and a large-scale time-course gene expression data on human influenza infection, we demonst

219 tively new and large datasets including gene expression data on hundreds of cell lines and their cyto

220 on, off-target analysis, integration of gene expression data, optimal thresholds for hit selection an

221 red regulatory elements in differential gene expression data or noncoding RNA discovery, as well as a

222 overlapped with published EMT cell line gene expression data (P < .05, FDR < 0.20).

223 Full-blood mRNA gene expression data, plasma IgE levels, and immune cell freq

224 ructing gene regulatory networks (GRNs) from expression data plays an important role in understanding

225 of massively parallel sequencing, genomewide expression data production has reached an unprecedented

226 alitative representation (discretization) of expression data, query-based biclustering, bicluster exp

227 istinguishing cell states based only on gene expression data remains a challenging task.

228 transporter protein (SGLT2) mRNA and protein expression data reported in the literature.

229 ltogether, DGET provides a flexible tool for expression data retrieval and analysis with short or lon

230 ovement, overcoming the limit of enforced co-expression data retrieval and instead enabling the retur

231 The expression data revealed a complex spatiotemporal patter

232 Finally, the analysis of gene expression data revealed a high correlation between elev

233 Gene ontology enrichment analysis from gene-expression data revealed a positive association of CD73

234 Analysis of genome-wide expression data revealed activation of the aryl hydrocar

235 Interrogation of human cancer gene expression data revealed that high TNC expression correl

236 Expression data revealed that Hydra's main bacterial col

237 A genome-wide association study on the expression data revealed that trans regulation seems to

238 utational integration with human genetic and expression data revealed the disease relevance of NPAS-r

239 ce for this association was provided by gene expression data: rs113288603 is associated with increase

240 ement our framework on a comprehensive miRNA expression data set for alcohol dependence and identify

241 Mining available gene expression data sets allowed to observe that high co-exp

242 or estimating aFC from both eQTL and allelic expression data sets and apply it to Genotype Tissue Exp

243 hinyGEO, that allows a user to download gene expression data sets directly from GEO in order to perfo

244 ed GCN from a compendium of 2,016 mixed gene expression data sets from five tumor subtypes obtained f

245 a weighted p-value analysis of two real gene expression data sets using gene sets drawn from MSigDB c

246 linical settings using five independent gene expression data sets.

247 nerate quantitative, spatially resolved gene expression data sets.

248 Cellular and expression data showed sensitivity of PNH-associated mut

249 alysis of human laminar-specific neocortical expression data showed that candidate genes are converge

250 es of known molecular interactions with gene expression data; such integration enables identification

251 Lastly, tumor expression data suggest that DPYD repression by Ezh2 pre

252 analysis of the uterine fibroid growth gene expression data suggests that molecular characteristics

253 atrix views; capabilities to filter and sort expression data summaries; a batch search utility; gene-

254 We present gene expression data supporting the hypothesis that Satb1 and

255 ied processing and normalization of raw gene expression data, systematic removal of batch effects, an

256 approach uncovers underlying factors of the expression data that are otherwise entangled or masked b

257 inical phenotypes using baseline genome-wide expression data that makes use of prior biological knowl

258 ny artifacts hidden in high-dimensional gene expression data that may negatively affect linear regres

259 method for the differential analysis of gene expression data that utilizes bootstrapping in conjuncti

260 egrated large-scale literature data and gene expression data that were acquired from both postmortem

261 (PPI) network data with tissue specific gene expression data to "find" SNPs of modest significance to

262 There is recent interest in using gene expression data to contextualize findings from tradition

263 els have been used to estimate GRN from gene expression data to distinguish direct interactions from

264 o created a public repository of sepsis gene expression data to encourage their future reuse.

265 s at each time point were compared with gene expression data to gain new insights into intracellular

266 performed multivariate analysis of the gene expression data to identify genes that predict UCP1.

267 ferential network using high-throughput gene expression data to identify GRN dynamics among different

268 egrated systems-level analysis of brain gene expression data to identify molecular networks disrupted

269 a systems-level analysis of genome-wide gene expression data to infer gene-regulatory networks conser

270 y to show how our map can be customized with expression data to pinpoint regulated subnetworks and dr

271 Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare

272 Our results highlight the potential of gene expression data to quantify effects of complex exposures

273 We mined gene expression data to uncover genes that are differentially

274 The biochemical and expression data together indicate that Bayberry surface

275 (PPI) network, we simulated microarray gene expression data under case and control conditions.

276 asynchronous Boolean models with single-cell expression data using a novel Boolean state space scorin

277 at the major source of variation in the gene expression data was driven by genotype, but we also obse

278 d proteomics data sets separately, only gene expression data was found to explain significant variati

279 thm to infer cell quantity changes from gene expression data, we found enrichment of distinct T cell

280 In four species with sufficient gene expression data, we identified 43 highly coexpressed clu

281 Using Braincloud mRNA expression data, we identified a robust and specific ass

282 interference RNA screens and additional gene expression data, we identified the transcription factor

283 By network analysis of gene expression data, we identified two gene modules that str

284 Through analysis of gene expression data, we provide evidence that the hedgehog p

285 nal statistical methods for analysis of gene-expression data, we show how it can detect changes in ge

286 omatin conformation capture and differential expression data, we show that CisMapper is more accurate

287 Incorporating genetic and expression data, we show that lncRNAs overlapping trait-

288 nostring human v2 miRNA microarray array and expression data were analyzed on nSolver analysis softwa

289 ACKR3 expression data were extracted from Cancer Cell Line Enc

290 PSMA expression data were extracted from publicly available g

291 vering gene co-expression networks from gene expression data, where each network encodes relationship

292 simply from high throughput single cell gene expression data, which should be widely applicable given

293 esearch Consortium and correlated RXFP1 gene expression data with cross-sectional clinical and demogr

294 etrix HTA 2.0 arrays to generate global gene expression data with doxycycline induction.

295 We further combined DXME gene expression data with eQTL data from the GTEx project and

296 the bovine genome, annotation, QTL, SNP and expression data with external sources of orthology, gene

297 edictive system (Mogrify) that combines gene expression data with regulatory network information to p

298 ontrolling this response, we integrated gene expression data with the chromatin landscape in the hypo

299 es that exhibit four subgroups, based on the expression data, with distinctive genomic features in te

300 tra expression states present in single-cell expression data without getting adversely affected by th