ライフサイエンスコーパス: gene expression data

1 f Drug Indications Using Compendia of Public Gene Expression Data".

2 genetic markers of expression and intestinal gene expression data).

3 e model captured intermediate and phenotypic gene expression data.

4 osed ensemble framework using yeast PINs and gene expression data.

5 ns, in C. elegans based on new and published gene expression data.

6 logical insight from complex and often noisy gene expression data.

7 the methodology by analyzing uterine fibroid gene expression data.

8 cell lineage relationships from single-cell gene expression data.

9 accurate assessment of protein activity from gene expression data.

10 an genes in a given tissue using time-course gene expression data.

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

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

13 st method for biomarker identification using gene expression data.

14 false positive control, taking advantage of gene expression data.

15 to the PhenomeExpress algorithm to interpret gene expression data.

16 atory interactions directly from time-series gene expression data.

17 each locus by analyzing epigenetic marks and gene expression data.

18 sing new direction for the interpretation of gene expression data.

19 we can identify cancer miRNAs directly from gene expression data.

20 the perturbed cellular signals by mining the gene expression data.

21 ets and the variance structure of microarray gene expression data.

22 ractions improve set-level classification of gene expression data.

23 ings by comparison to orthogonal single-cell gene expression data.

24 equences and can incorporate tissue-specific gene expression data.

25 own, often unavoidable systematic changes to gene expression data.

26 facilitate integrative modeling of multiple gene expression data.

27 guration of promoter states from single-cell gene expression data.

28 based on the integration of interactome and gene expression data.

29 correlation network arising from large-scale gene expression data.

30 statistically comparable but not superior to gene expression data.

31 s to confidently evaluate the reliability of gene expression data.

32 osed methodology by several meta-analysis of gene expression data.

33 an often encountered problem, especially for gene expression data.

34 cancer in a collection of 4,801 breast tumor gene expression data.

35 uction of gene regulatory networks, based on gene expression data.

36 meta-analysis was used as a source for brain gene expression data.

37 cancer and immune cell types from bulk tumor gene expression data.

38 s and genes from GWAS summary statistics and gene expression data.

39 f a gene regulatory network from single-cell gene expression data.

40 arn the hierarchical structure within cancer gene expression data.

41 orrelation coefficient (PCC) calculated from gene expression data.

42 or' transcription factor just using only the gene expression data.

43 clinically meaningful abstractions of cancer gene expression data.

44 ponse across different individuals, based on gene expression data.

45 fective for classification than the original gene expression data.

46 physical clusters of co-regulated genes from gene expression data.

47 ) applied to genome-wide DNA methylation and gene expression data across 763 primary samples identifi

48 ny meta-analysis methods designed to combine gene expression data across many tissues to increase pow

49 chromatin immunoprecipitation sequencing and gene expression data allowed us to derive a high-confide

50 Evaluation of this gene expression data allowed us to predict several genes

51 Gene expression data analysis also suggests that the non

52 Based on gene expression data analysis, we found that MSI2 expres

53 hanistic-anchored approach to single-subject gene expression data analysis.

54 ined the model on a set of 236 patients with gene expression data and clinical information, and valid

55 hierarchical model integrating genetics and gene expression data and combining this with survival an

56 CellNet takes as input gene expression data and compares them with large data s

57 es the integration of multiple types of both gene expression data and database schema.

58 r construction of co-expression network from gene expression data and for extraction of densely conne

59 ., enhancer hijacking) by integrating SCNAs, gene expression data and information on topologically as

60 cting lineage relationships from single-cell gene expression data and modeling the dynamic changes as

61 Using gene expression data and pathway signatures, we predicte

62 biological differences between samples using gene expression data and pre-defined biological pathway

63 By integrating gene expression data and protein-protein interaction dat

64 emble framework is proposed which integrates gene expression data and protein-protein interaction net

65 However, high noise levels in gene expression data and relatively high correlation bet

66 odological issues related to the analysis of gene expression data and social gradients in health and

67 rk, LogicTRN can naturally integrate dynamic gene expression data and TF-DNA-binding signals in order

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

69 ion of cis-acting eQTLs by analyzing RNA-seq gene-expression data and genome-wide high-density genoty

70 bsolute cell fraction predictions from tumor gene expression data, and provides a unique novel experi

71 We consider yeast cell-cycle gene expression data, and show that the proposed method

72 rs for each species by a cluster analysis of gene expression data, and subsequently computes the over

73 T cells in molecular networks built with our gene expression data, and we confirmed upregulation of M

74 grated analyses of genomic binding of MeCP2, gene-expression data, and patterns of DNA methylation.

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

76 Gene expression data are accumulating exponentially in p

77 Studies that combine genomic and gene expression data are scarce, however, particularly i

78 Finally, matched gene expression data are used to identify, besides diffe

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

80 more popular for analyzing high dimensional gene expression data as it allows us to borrow informati

81 fic Ocean as a model system, we used cardiac gene expression data (as a proxy for thermal tolerance t

82 ication designed for exploratory analysis of gene expression data, as well as data from related exper

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

84 We evaluated prediction of gene expression data based on 133 studies, sourced from

85 -based feature selection method for temporal gene expression data based on maximum relevance and mini

86 g and scoring regulator networks upstream of gene-expression data based on a large-scale causal netwo

87 biological differences between samples using gene expression data by assuming that ontologically defi

88 de an effective and efficient way to analyze gene expression data by finding a group of genes with tr

89 ular composition of a solid tumour from bulk gene expression data by mathematical deconvolution, usin

90 We show that gene expression data can be computationally constructed,

91 Gene set analysis (GSA) of gene expression data can be highly powerful when the bio

92 Gene expression data can help researchers understand the

93 We used gene expression data collected from whole blood from 862

94 Negligence at any step can lead to gene expression data containing inadequate or composite

95 As the amount of publicly available gene expression data continues to grow, our method will

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

97 Gene expression data corroborated K deficiency in the nr

98 Comparisons with primary mouse and human gene expression data demonstrated rostral and caudal pro

99 Current options to mine publicly available gene expression data deposited in NCBI's gene expression

100 y, the bioinformatics analysis of microarray gene expression data derived from a set of high-grade hu

101 ntrol in 1,170 adults with asthma, each with gene expression data derived from either whole blood (WB

102 rom breast tumors and time-course microarray gene expression data derived from in-vivo muscle regener

103 Paired microarray gene expression data derived from peripheral blood monon

104 e computational tools used, the inclusion of gene expression data does not improve the prediction qua

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

106 ngineering of transcriptional networks using gene expression data enables identification of genes tha

107 ome-wide regulatory landscapes from temporal gene expression data, especially for complex eukaryotes

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

109 Indeed, power improved when gene expression data for FDR-controlled informative weig

110 E algorithm to facilitate the application of gene expression data for generating new hypotheses on tr

111 chiatric Genomics Consortium, along with the gene expression data for multiple tissues from the Genot

112 ait loci (cQTLs) using parallel genotype and gene expression data for segregants from a cross between

113 erested in exploring the prognostic value of gene expression data for specific subtypes or stages of

114 Gene expression data for the 100 subclass-defining genes

115 e annotation, anatomical ontologies and some gene expression data for the six species with highest qu

116 VM) model, trained using brain developmental gene expression data, for the classification and priorit

117 Competing teams used gene expression data from 26 stimuli to develop protein

118 and sequencing experiments with genotype and gene expression data from 602 prostate tumor samples.

119 to breast cancer gene expression data and to gene expression data from a cardiovascular study cohort,

120 tified target gene list and combines it with gene expression data from a context, quantifying that re

121 ckage that provides an interface to simulate gene expression data from a given gene network using the

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

123 algorithm in practice using the time-course gene expression data from a study on human respiratory e

124 ere, we show, using longitudinal whole-blood gene expression data from a twin cohort, that the geneti

125 VIP platform will facilitate the analysis of gene expression data from a wide variety of species by e

126 Analyses of global gene expression data from adipose tissue, skeletal muscl

127 Gene expression data from an open public data set was al

128 Using publicly available gene expression data from Arabidopsis (Arabidopsis thali

129 uick implementation for generating realistic gene expression data from biologically relevant networks

130 Using the large collection of drug-induced gene expression data from Connectivity Map, several drug

131 s (breast and colorectal cancers) by merging gene expression data from different studies after diagno

132 iation studies (GWAS) and massive amounts of gene expression data from different tissues.

133 lished and trained with perturbation-derived gene expression data from five hepatocyte donors.

134 We generated gene expression data from fluorescence-activated cell so

135 ichment Analysis) of high-density microarray gene expression data from formalin-fixed paraffin-embedd

136 Using gene expression data from GBM stem-like cells, astrocyte

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

138 Analysis of global gene expression data from HOXA5-depleted MCF10A breast e

139 Systematic analysis of gene expression data from human liver undergoing hepatic

140 f EC-tagging by obtaining cell type-specific gene expression data from intact Drosophila larvae, incl

141 We focus on the problem of finding eGenes in gene expression data from many tissues, and show that ou

142 Using gene expression data from mice and humans with mitochond

143 ach, which will facilitate the future use of gene expression data from open databases to reveal novel

144 idated with predesigned mixtures, CAM on the gene expression data from peripheral leukocytes, brain t

145 We also analyze gene expression data from post-trauma patients, allowing

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

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

148 o identify cancer-specific targets utilizing gene expression data from TCGA (The Cancer Genome Atlas)

149 This network was scored with gene expression data from term and preterm myometrium to

150 By analyzing post-mortem gene expression data from the Allen Brain Atlas, we inve

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

152 e tested GEMINI on breast and ovarian cancer gene expression data from The Cancer Genome Atlas projec

153 We analyzed clinical and gene expression data from The Cancer Genome Atlas, Oncom

154 P-values using both noisy simulated data and gene expression data from The Cancer Genome Atlas.

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

156 patients, by extracting RNA-sequencing-based gene expression data from the TCGA-GBM database.

157 have conducted a study of publicly available gene expression data from three cohorts of ACC patients

158 Furthermore, gene-expression data from feathers of different bird spe

159 stem cell (LSC) gene expression profiles and gene expression data generated from JAM-C-expressing leu

160 literature data, including a large amount of gene expression data, Genome-Wide Association Studies ca

161 Reconstructions of gene networks from gene expression data greatly facilitate our understandin

162 rstand fundamental biological processes from gene expression data has grown in parallel with the rece

163 Set-level classification of gene expression data has received significant attention

164 ion of chromatin accessibility profiles with gene expression data identified unique regulatory module

165 Gene expression data imply that under low pH conditions,

166 e-shaped tissues and organs from single-cell gene expression data in 3D space.

167 ion-based algorithm by step-wise analysis of gene expression data in 558 blood samples from 436 renal

168 We investigated exome sequencing and gene expression data in 663 and 711 white and 105 and 15

169 tely, it is becoming more feasible to obtain gene expression data in addition to genotypes and phenot

170 study demonstrates that even a small set of gene expression data in addition to sequence homologies

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

172 MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenoc

173 have been successfully applied for analyzing gene expression data in cross-sectional studies.

174 We generated a uniquely large resource of gene expression data in four interconnected limbic brain

175 oice for analyzing omics data in general and gene expression data in particular.

176 ion (DESeq) or Empirical analysis of Digital Gene Expression data in R (edgeR) for RNA-seq profiling.

177 However, the scarcity of gene expression data in reptiles, crucial for understand

178 d a systematic search for publicly available gene expression data in sepsis and tested each gene expr

179 ell lines and applying these models to tumor gene expression data in the clinical data sets (e.g., TC

180 fer the regulatory circuits from single-cell gene expression data, in a holistic way.

181 from those that induce IL-13 production, and gene expression data indicate that an alternative activa

182 Analysis of breast cancer gene expression data indicates that HOTAIR is co-express

183 lly, findings suggest promise in integrating gene expression data into population studies and provide

184 robust markers for cancer prognosis based on gene expression data is an important yet challenging pro

185 Distance based unsupervised clustering of gene expression data is commonly used to identify hetero

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

187 udy about the identification of modules from gene expression data mapped on protein interaction netwo

188 en layer of a deep learning model trained on gene expression data may represent signals related to tr

189 enomics-the integration of GWAS signals with gene expression data-may illuminate genes and gene netwo

190 We apply our method to empirical gene expression data measured in 373 European individual

191 o phenotypic traits of interest, we analyzed gene expression data, metabolite data obtained with GC-M

192 th genome-wide DNA methylation (n = 124) and gene expression data (n = 297) before and after exposure

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

194 s such as microbial community composition or gene expression data, observations can be generated from

195 maize candidate genes based on the empirical gene expression data obtained by RT-qPCR technique from

196 We analyzed gene expression data obtained from the Lung Tissue Resea

197 clustering approach which first denoises the gene expression data of each species into a data matrix.

198 on the NanoString platform, using microarray gene expression data of matched fresh frozen biopsies as

199 hniques is demonstrated on numerically mixed gene expression data of ovarian cancer subtypes.

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

201 thRings permits an overview of the impact of gene expression data on all pathways to facilitate visua

202 pression signature sets were validated using gene expression data on BE and esophageal adenocarcinoma

203 lated datasets and a large-scale time-course gene expression data on human influenza infection, we de

204 relatively new and large datasets including gene expression data on hundreds of cell lines and their

205 ization, off-target analysis, integration of gene expression data, optimal thresholds for hit selecti

206 ructured regulatory elements in differential gene expression data or noncoding RNA discovery, as well

207 nteractions from biological datasets such as gene expression data or sequence ensembles.

208 hes tend not to exploit the full spectrum of gene expression data, or the various relationships and d

209 Using human genetics data and postmortem gene expression data, our approach can correctly identif

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

211 rs use massive amounts of publicly available gene expression data (PED) to make discoveries.

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

213 evidence from other exome array studies and gene expression data points toward potential involvement

214 Single-cell gene expression data provide invaluable resources for sy

215 While these gene expression data provide novel insights into identif

216 In addition, it allows for mapping the gene expression data, providing information of transcrip

217 h-throughput technologies making large-scale gene expression data readily available, developing appro

218 Distinguishing cell states based only on gene expression data remains a challenging task.

219 the most meaningful substructures hidden in gene expression data remains a highly challenging proble

220 gulatory networks (GRNs) from time series of gene expression data remains an important open problem i

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

222 Network analyses of gene expression data revealed additional candidate trans

223 ntegration of the RUNX1 binding profile with gene expression data revealed an unexpected early role f

224 Interrogation of human cancer gene expression data revealed that high TNC expression c

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

226 Integration of binding and gene expression data reveals a concise set of target gen

227 vidence for this association was provided by gene expression data: rs113288603 is associated with inc

228 We have assembled a large gene expression data set assembled from multiple studies

229 We developed a large-scale gene-expression data set from several hundred single bra

230 Mining available gene expression data sets allowed to observe that high c

231 relies on computational methods that utilize gene expression data sets and knockout fitness data sets

232 e suggest applying DupChecker to examine all gene expression data sets before any data analysis step.

233 on, shinyGEO, that allows a user to download gene expression data sets directly from GEO in order to

234 ive than other alternatives, to screen large gene expression data sets for conserved and differential

235 Evaluation of gene expression data sets from developing and adult huma

236 notated GCN from a compendium of 2,016 mixed gene expression data sets from five tumor subtypes obtai

237 advances facilitate the generation of large gene expression data sets in high-throughput format.

238 Meta-analysis of gene expression data sets is increasingly performed to h

239 rces required to compare tens of genomes and gene expression data sets make this type of analysis dif

240 Applying it to gene expression data sets of gastric cancer (GC), we com

241 Here, we utilized available gene expression data sets of selected brain regions of H

242 Gene expression data sets revealed that primary human no

243 researchers to easily navigate large complex gene expression data sets to determine important feature

244 and a weighted p-value analysis of two real gene expression data sets using gene sets drawn from MSi

245 ent clinical settings using five independent gene expression data sets.

246 ally, to perform a meta-analysis of multiple gene expression data sets.

247 lassification tasks using public prokaryotic gene expression data sets.

248 to generate quantitative, spatially resolved gene expression data sets.

249 sing computational approaches to explore our gene-expression data sets, we found that NRAS(G12V) enfo

250 s of independently acquired histological and gene-expression data showed that nodal participation col

251 tperforms traditional models based on single gene expression data sources".

252 tabases of known molecular interactions with gene expression data; such integration enables identific

253 Our analysis of the uterine fibroid growth gene expression data suggests that molecular characteris

254 We present gene expression data supporting the hypothesis that Satb

255 unified processing and normalization of raw gene expression data, systematic removal of batch effect

256 On the other hand, there are huge amount of gene expression data that are publicly accessible.

257 er many artifacts hidden in high-dimensional gene expression data that may negatively affect linear r

258 h, a method for the differential analysis of gene expression data that utilizes bootstrapping in conj

259 d integrated large-scale literature data and gene expression data that were acquired from both postmo

260 tion (PPI) network data with tissue specific gene expression data to "find" SNPs of modest significan

261 e genomics of closely related organisms with gene expression data to assemble large-scale TRN models

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

263 We use gene expression data to describe four molecular subtypes

264 effect when we combined prior knowledge and gene expression data to discover regulatory networks.

265 l models have been used to estimate GRN from gene expression data to distinguish direct interactions

266 e also created a public repository of sepsis gene expression data to encourage their future reuse.

267 utions at each time point were compared with gene expression data to gain new insights into intracell

268 s and performed multivariate analysis of the gene expression data to identify genes that predict UCP1

269 r differential network using high-throughput gene expression data to identify GRN dynamics among diff

270 n integrated systems-level analysis of brain gene expression data to identify molecular networks disr

271 rmed a systems-level analysis of genome-wide gene expression data to infer gene-regulatory networks c

272 data from post-trauma patients, allowing the gene expression data to provide a molecularly driven phe

273 Our results highlight the potential of gene expression data to quantify effects of complex expo

274 We mined gene expression data to uncover genes that are different

275 focused on estimating gene networks based on gene expression data to understand the functional basis

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

277 ng gene regulatory networks from time series gene expression data using the Granger causality (GC) mo

278 nd that the major source of variation in the gene expression data was driven by genotype, but we also

279 MS and proteomics data sets separately, only gene expression data was found to explain significant va

280 Using yeast gene expression data we show how correlation can be misl

281 y applying our sixmer-based approach on rice gene expression data we show that it can accurately pred

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

283 By integrating DNA methylation and gene expression data, we found that hypermethylation of

284 In four species with sufficient gene expression data, we identified 43 highly coexpresse

285 ith the comparative analysis of proteome and gene expression data, we identified TCF7 as a promising

286 hese interference RNA screens and additional gene expression data, we identified the transcription fa

287 By network analysis of gene expression data, we identified two gene modules tha

288 Consistent with this gene expression data, we observed reduced ERK activation

289 Through analysis of gene expression data, we provide evidence that the hedge

290 ditional statistical methods for analysis of gene-expression data, we show how it can detect changes

291 lly informed by species-specific time series gene expression data, when available, using Gaussian pro

292 recovering gene co-expression networks from gene expression data, where each network encodes relatio

293 ined simply from high throughput single cell gene expression data, which should be widely applicable

294 from gene lists; to perform meta-analysis on gene expression data while taking into account multiple

295 sue Research Consortium and correlated RXFP1 gene expression data with cross-sectional clinical and d

296 Affymetrix HTA 2.0 arrays to generate global gene expression data with doxycycline induction.

297 We further combined DXME gene expression data with eQTL data from the GTEx projec

298 a predictive system (Mogrify) that combines gene expression data with regulatory network information

299 ces controlling this response, we integrated gene expression data with the chromatin landscape in the

300 y network structure from limited time series gene expression data, without any a priori knowledge of