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 rediction by taking advantage of time-course gene expression data.

3 as introduced new opportunities in analyzing gene expression data.

4 successfully characterize the full suite of gene expression data.

5 tive regulatory mechanisms form differential gene expression data.

6 s prognostic as the collection of individual gene expression data.

7 fast and simple quantitative querying of the gene expression data.

8 rder to set a standard for the annotation of gene expression data.

9 s of transcription factor (TF) activity from gene expression data.

10 expression analyses to make discoveries from gene expression data.

11 using curated publicly available microarray gene expression data.

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

13 ations from the high dimensional and complex gene expression data.

14 e model captured intermediate and phenotypic gene expression data.

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

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

17 lysis on publicly available, high-throughput gene expression data.

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

19 arn the hierarchical structure within cancer gene expression data.

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

21 oring such relationship from cross-sectional gene expression data.

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

23 clinically meaningful abstractions of cancer gene expression data.

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

25 fective for classification than the original gene expression data.

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

27 ments, and annotated this model with matched gene expression data.

28 apped onto human organs using organ-specific gene expression data.

29 useful dynamic information from time-series gene expression data.

30 e epidemiological analysis was obtained from gene expression data.

31 ality or compression algorithm for analyzing gene expression data.

32 ied by a fully unsupervised deconvolution of gene expression data.

33 ct downstream pathways that best explain the gene expression data.

34 ted, largely due to limited primary human TE gene expression data.

35 70 individuals, who also have bulk cortical gene expression data.

36 ir underlying molecular biology derived from gene expression data.

37 g survival times simulated from uncorrelated gene expression data.

38 ons to large toxicogenomics and differential gene expression data.

39 analysis (ssGSEA) method to derive RDIs with gene expression data.

40 network changes across time from time series gene expression data.

41 s) and validated our findings with published gene expression data.

42 f the biological factors of variation in the gene expression data.

43 lexible approach for harmonizing large-scale gene-expression data.

44 tory networks are typically constructed from gene expression data acquired following genetic perturba

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

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

47 In this work, we evaluate how network and gene expression data affect ProTINA's accuracy.

48 This together with publicly available gene expression data allows for a focused search for unc

49 Pseudotime estimation from single-cell gene expression data allows the recovery of temporal inf

50 Here, we use ten time points of gene expression data along with gene network modeling to

51 The gene expression data analysis allowed identifying strain

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

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

54 orm for resources, including variation data, gene expression data and genetic markers.

55 lso allows users to overlay gene annotation, gene expression data and genome methylation data on top

56 performs network-constrained biclustering on gene expression data and identifies gene modules - conne

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

58 Both raw whole blood gene expression data and informative gene modules genera

59 modeling predicted phenotypes in repurposed gene expression data and modeling predicted causes of de

60 We used normative adult brain gene expression data and partial least squares analysis

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

62 y our methods to the integrative analysis of gene expression data and protein abundance data from the

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

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

65 This mechanism illuminates puzzling gene expression data and suggests novel engineering stra

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

67 curately predicted using developmental brain gene expression data and transcript sequence features, a

68 Both developmental brain gene expression data and transcript sequence were found

69 ance spike seed setting and grain size using gene expression data and were validated in three bi-pare

70 cxds, utilizes binarized (absence/presence) gene expression data and, employing a binomial model for

71 emented for a representation learning of the gene expression data, and a random-forest-based feature

72 l networks reconstructed by the RTN package, gene expression data, and a two-tailed Gene Set Enrichme

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

74 yses included Independent Sample t-tests for gene expression data, and supervised multi-variate analy

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

76 ad and analyze their omics data, such as the gene-expression data, and overlay curated or experimenta

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

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

79 However, a major challenge is that gene-expression data are frequently contaminated by many

80 mple of high-dimensional data (for instance, gene expression data) are ubiquitous in modern bioinform

81 k-based approach that takes high-dimensional gene expression data as input and performs non-linear ma

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

83 able method for obtaining spatially resolved gene expression data at resolutions comparable to the si

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

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

86 for running cell-based models and simulating gene expression data based on the model states.

87 StanDep clusters gene expression data based on their expression pattern a

88 framework has been successful in analysis of gene expression data, but application to epigenetic data

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

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

91 ular embedding of environmental factors from gene expression data by using latent variable (LV) analy

92 are accurately predicted, the corresponding gene expression data can be reliably used in downstream

93 Gene expression data can help researchers understand the

94 We integrate these findings with genome-wide gene expression data collected from the same human liver

95 apply these two methods to a set of RNA-seq gene expression data collected in a breast cancer study.

96 Knockout gene expression data confirmed ~60-70% of predictions of

97 w that technical and biological artifacts in gene expression data confound commonly used network reco

98 datasets including both DNA methylation and gene expression data demonstrate favorable performance o

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

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

101 ackage by reanalyzing available enhancer and gene expression data derived from ependymoma brain tumor

102 We analyzed developmental time-course gene expression data derived from human pluripotent stem

103 Paired microarray gene expression data derived from peripheral blood monon

104 (GRNs) by combining enhancer methylation and gene expression data derived from the same sample set.

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

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

107 s in non-model species where rich sources of gene expression data exist, but annotation rates are poo

108 With time-series gene expression data, exploiting inherent time informati

109 supervised compression algorithms applied to gene expression data extract latent or hidden signals re

110 Using an encoding for gene expression data, followed by deep neural networks a

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

112 fic CRMs based on TF-gene binding events and gene expression data for a particular cell type.

113 M is a new powerful tool for the analysis of gene expression data for cancer diagnosis.

114 phylogenetic analysis and publicly available gene expression data for each gene family, which will ai

115 integrated the available DNA methylation and gene expression data for HPV + OPSCC samples to filter t

116 e have focussed on the latter by integrating gene expression data for the in vitro differentiation of

117 al structure; direct access to the wild-type gene expression data for the tissues affected in a speci

118 roposed tool, PathTracer, is demonstrated on gene expression data from 1952 invasive breast cancer sa

119 Using patients' tumor gene expression data from 4 independent data sets, we co

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 o analyze paired chromatin accessibility and gene expression data from a retinoic acid (RA)-induced m

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

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

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

126 Gene expression data from approximately 10,000 tumor sam

127 A metaanalysis of gene expression data from B-ALL patient specimens reveal

128 Gene expression data from beta-NGF stimulated cartilage

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

130 We apply our approach to gene expression data from both synthetic and real (breas

131 We analysed EC gene expression data from boy-girl twins at birth and in

132 to perform integrative analysis of SCNAs and gene expression data from breast cancer and 18 additiona

133 e we effectively integrated metabolomics and gene expression data from breast cancer mouse models thr

134 Here, we perform a new analysis on gene expression data from cells in bronchoalveolar lavag

135 Integrating gene expression data from colon tumors with other gene e

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

137 d information from published literature with gene expression data from diverse model systems to infer

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

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

140 by genomic copy number variations (CNVs) and gene expression data from healthy subjects.

141 Liver gene expression data from human patients reveal that Gps

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

143 ametocytes, and will be useful for analyzing gene expression data from laboratory and field samples.

144 By using a unique set of gene expression data from lung cells obtained using bron

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

146 Using gene expression data from mice and humans with mitochond

147 f ACE2 and TMPRSS2 in 2 data sets containing gene expression data from nasal and airway epithelial ce

148 nes were externally validated using platelet gene expression data from patients with coronary atheros

149 Analysis of gene expression data from patients with neuroblastoma re

150 Here, we build machine learning models using gene expression data from patients' primary tumor tissue

151 Gene expression data from pretreatment biopsies of patie

152 oci (eQTLs) in humans and chimpanzees, using gene expression data from primary heart samples.

153 When compared with whole blood gene expression data from seven external datasets contai

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

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

156 dren and 87 children with ADHD) and cortical gene expression data from the Allen Institute for Brain

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

158 We applied SPONGE to paired miRNA and gene expression data from The Cancer Genome Atlas for st

159 Mutation, copy number alteration and gene expression data from The Cancer Genome Atlas projec

160 ognostic prediction of 15 cancer types using gene expression data from The Cancer Genome Atlas, as we

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

162 arge-scale pharmacogenomics study with basal gene expression data from the CCLE project and prior kno

163 Taken together, gene expression data from the de-novo transcriptome of I

164 ted and analyzed the clinical and microarray gene expression data from those individuals to understan

165 We compress gene expression data from three large datasets consistin

166 Moreover, we leveraged gene expression data from three separate cell types (mon

167 yed machine learning approaches to integrate gene expression data from three SLE data sets and used i

168 43,000 genotyped individuals with associated gene expression data from ~51,000 experiments, yielding

169 Analysis of gene-expression data from 159 metastatic CRPC samples an

170 files to growth rates by gathering published gene-expression data from Escherichia coli and Saccharom

171 publicly available ChIP-seq experiments with gene-expression data from tissue-specific RNA-seq experi

172 showcased robust and superior performance on gene expression data generated by microarray, bulk RNA-S

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

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

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

176 The biclustering of large-scale gene expression data holds promising potential for detec

177 Analysis of gene expression data identified biomarkers associated wi

178 WAS statistics of kidney function traits and gene expression data identified relevant tissues and cel

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

180 ers previously obtained through longitudinal gene expression data, implying that differential treatme

181 by using samples with matched genotypes and gene expression data in a given tissue.

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

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

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

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

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

187 ression network-based functional analysis of gene expression data in the hippocampal subfields, CA1 a

188 a that exhibits properties reflected in real gene expression data, including main effects and network

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

190 ey can embed complex, noisy high-dimensional gene expression data into a low-dimensional latent space

191 Time-course gene expression data is a rich source of information tha

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

193 In these studies, the gene expression data is either considered at one time-po

194 ikely that the dynamic nature of time-series gene expression data is more informative in predicting c

195 Sample classification of gene expression data is one such popular bio-data analys

196 of gene regulatory networks from time series gene-expression data is a challenging problem, not only

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

198 cs data sets illustrating the integration of gene expression data, lipid concentrations and methylati

199 g a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images fr

200 xecution of all analysis steps starting with gene expression data matrix.

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

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

203 help understand cell type composition using gene expression data, methods of estimating (deconvolvin

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

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

206 Gene expression data of breast tumors and normal tissues

207 tionally estimate cell-type proportions from gene expression data of bulk blood samples, but their pe

208 However, most models use bulk gene expression data of entire tumor biopsies, ignoring

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

210 L to combine clinical information with miRNA gene expression data of ovarian cancer study into a sing

211 atic mutation data, DNA methylation data and gene expression data of three cancer types from The Canc

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

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

214 bits it from being used for large microarray gene expression data or any other large data set, which

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

216 reconciles the level of ITH and CTC-derived gene expression data outperformed the initial classifier

217 Antarctic clam (Laternula elliptica) mantle gene expression data produced over an age-categorized sh

218 sity data (SNPversity), download and compare gene expression data (qTeller), visualize pedigree data

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

220 We applied SArKS to published gene expression data representing distinct neocortical n

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

222 Deconvolution of primary tumor gene expression data revealed a strong association betwe

223 Integration with chondrogenesis gene expression data revealed an enrichment of significa

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 Gene-expression data revealed that, in NKT cells, glucos

227 Further integrating gene expression data reveals evidence of cis CpG-transcr

228 ssed pediatric septic shock biomarkers using gene expression data sampled from 181 patients admitted

229 se include: improvements to the Differential Gene Expression Data Search, facilitating searches for g

230 e show how to use this package to simulate a gene expression data set and consequently benchmark anal

231 Applying ARTDeco to a gene expression data set from IAV-infected monocytes fro

232 yed strong disease signals in an independent gene expression data set of COPD and IPF lung tissue and

233 taking advantage of a unique tissue-specific gene expression data set, we showed that the majority of

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

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

236 Gene expression data sets of Multiple tissues and Yeast

237 ch used the vast array of publicly available gene expression data sets to query similarity to CCNB1,

238 approach to toxicogenomics and schizophrenia gene expression data sets.

239 d (4) reverse-engineered regulons from large gene expression data sets.

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

241 redundant marker genes from high dimensional gene expression data sets.

242 Our gene expression data suggest that malR expression is rep

243 induction of chondrocyte calcification, and gene expression data suggested that the Indian Hedgehog-

244 Differential gene expression data suggested the presence of extensive

245 We present gene expression data supporting the hypothesis that Satb

246 Our gene expression data supports this hypothesis and low E2

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

248 lable to perform an array of analyses on the gene expression data that results from such studies.

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

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

251 lying the weighted models in the Alzheimer's gene expression data, the number of DE genes decreased i

252 Applied to gene expression data, the test reveals that the strength

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

254 bination of pathway network information with gene expression data to determine the degree to which a

255 's sensitivity analysis) to existing hepatic gene expression data to determine the role of transcript

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

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

258 val modeling approach and publicly available gene expression data to identify a minimal number of gen

259 elop an algorithm that can mine differential gene expression data to identify candidate cell type-spe

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

261 telligence with brain tissue and single cell gene expression data to identify tissues and cell types

262 TFactor, that combines network analyses with gene expression data to identify transcription factors (

263 D GWAS summary statistics with summary-level gene expression data to infer differential gene expressi

264 lity of tuxnet when using different types of gene expression data to infer networks and its accessibi

265 When applied to single-cell gene expression data to investigate mouse medulloblastom

266 The integration of gene expression data to predict systemic lupus erythemat

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

268 u promoter capture Hi-C, in conjunction with gene expression data to reveal likely target genes of th

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

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

271 d to infer two gene networks separately from gene expression data under two different conditions, and

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

273 technology developed for linear modeling of gene expression data was used in combination with therma

274 Also, incorporating gene expression data, we define a topic activity score t

275 erlaying chromatin structure information and gene expression data, we find evidence for venom gene-sp

276 Coupled with gene expression data, we found that genes near ruminant

277 Using prior brain-wide gene expression data, we found that the cortical map of

278 By analyzing published gene expression data, we found that the dynamics produce

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

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

281 Coupled with gene expression data, we identify a candidate male-steri

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

283 gning follow-up experiments for longitudinal gene expression data, weather pattern changes over time,

284 Normalized gene expression data were clustered and used as the inpu

285 Gene expression data were generated using Affymetrix HG-

286 In both cases, potencies derived from multi-gene expression data were highly correlated with orthogo

287 asible to generate large-scale, multi-tissue gene expression data, where expression profiles are obta

288 e now rich resources for tissue-level (bulk) gene expression data, which have been collected from tho

289 easurements allowing seamless integration of gene expression data with 13C data.

290 scriptome-wide association studies integrate gene expression data with common risk variation to ident

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

292 We used co-expression modules inferred from gene expression data with five methods as traits in tran

293 esolution with a focus on the integration of gene expression data with other types of single-cell mea

294 ates that statistically sound combination of gene expression data with prior knowledge about biology

295 nformatics approach, which connects the HSPC gene expression data with the candidate cargo in stimula

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

297 The EPEM is used to cluster temporal gene expression data with this additional variability.

298 te or computationally intensive for temporal gene expression data with this additional variability.

299 to infer gene networks and perform MRA from gene expression data, with optional corrections for copy

300 Concordant patterns are apparent in gene expression data, with regional differentiation foll