ライフサイエンスコーパス: expression data

1 ement over commonly used methods for RNA-seq expression data.

2 d linear mixed effects models for correlated expression data.

3 largely due to limited primary human TE gene expression data.

4 ndividuals, who also have bulk cortical gene expression data.

5 derlying molecular biology derived from gene expression data.

6 vival times simulated from uncorrelated gene expression data.

7 o large toxicogenomics and differential gene expression data.

8 sis (ssGSEA) method to derive RDIs with gene expression data.

9 rk changes across time from time series gene expression data.

10 d validated our findings with published gene expression data.

11 g us to extract distinguishing features from expression data.

12 biological factors of variation in the gene expression data.

13 tion by taking advantage of time-course gene expression data.

14 troduced new opportunities in analyzing gene expression data.

15 essfully characterize the full suite of gene expression data.

16 curves with a model built on dose-dependent expression data.

17 regulatory mechanisms form differential gene expression data.

18 for dimensionality reduction of single-cell expression data.

19 gnostic as the collection of individual gene expression data.

20 able, biological hypotheses from time-series expression data.

21 at efficiently constructs gene networks from expression data.

22 ase, animal-model, and osteoarthritis tissue expression data.

23 10059974) trial with pretreatment PTGS2 expression data.

24 logically relevant meaning from differential expression data.

25 and simple quantitative querying of the gene expression data.

26 veloped to mine gene-gene relationships from expression data.

27 s from the high dimensional and complex gene expression data.

28 nalysis currently relies primarily upon exon expression data.

29 , and annotated this model with matched gene expression data.

30 on publicly available, high-throughput gene expression data.

31 such relationship from cross-sectional gene expression data.

32 s that can complement protein-protein and co-expression data.

33 lihood framework by integrating mutation and expression data.

34 onto human organs using organ-specific gene expression data.

35 wnstream pathways that best explain the gene expression data.

36 developmental and stage-dependent network of expression data.

37 ul dynamic information from time-series gene expression data.

38 demiological analysis was obtained from gene expression data.

39 or compression algorithm for analyzing gene expression data.

40 le approach for harmonizing large-scale gene-expression data.

41 hen working on big data such as whole-genome expression data.

42 y a fully unsupervised deconvolution of gene expression data.

43 networks are typically constructed from gene expression data acquired following genetic perturbation

44 grated analysis of somatic mutations and RNA expression data across 12 tumor types reveals that mutat

45 des a statistical framework for interpreting expression data across species and in disease.

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

47 Pseudotime estimation from single-cell gene expression data allows the recovery of temporal informat

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

49 lization and transformation of miRNA and PCG expression data, along with the option to utilize predic

50 Postmortem DLPFC expression data analysis showed decreased expression lev

51 ed IRIS-EDA, which is a Shiny web server for expression data analysis.

52 cted a lung cancer-specific database housing expression data and clinical data from over 6700 patient

53 characterizing metabolism using single cell expression data and defines principles of the tumor micr

54 We provide composite and isoform transcript expression data and demonstrate diversity in isoform com

55 the world to browse segment-specific protein expression data and download them for their own research

56 INDRA-IPM contextualizes models with expression data and exports them to standard formats.

57 e used subtype signatures to deconvolute SOC expression data and found substantial intra-tumor NGH.

58 llows users to overlay gene annotation, gene expression data and genome methylation data on top of 3D

59 linical data, mRNA expression data, microRNA expression data and histopathology whole slide images (W

60 rms network-constrained biclustering on gene expression data and identifies gene modules - connected

61 th the latest genome versions, provide novel expression data and identify targets for future research

62 Both raw whole blood gene expression data and informative gene modules generated b

63 atient using cost-effective microarray-based expression data and machine learning algorithms could gr

64 hromatin accessibility data with large tumor expression data and model the effect of enhancers on tra

65 ling predicted phenotypes in repurposed gene expression data and modeling predicted causes of death i

66 Applying time course expression data and parsimonious gene correlation networ

67 We used normative adult brain gene expression data and partial least squares analysis to fi

68 anatomy browser that now provides access to expression data and phenotype data for a given anatomica

69 methods to the integrative analysis of gene expression data and protein abundance data from the NCI-

70 This mechanism illuminates puzzling gene expression data and suggests novel engineering strategie

71 ely predicted using developmental brain gene expression data and transcript sequence features, and th

72 Both developmental brain gene expression data and transcript sequence were found to co

73 in these tissues or cell types using public expression data and use deltaSVM scores as weights in th

74 spike seed setting and grain size using gene expression data and were validated in three bi-parental

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

76 s of a data simulator that creates realistic expression data, and a power assessor that provides a co

77 ed for a representation learning of the gene expression data, and a random-forest-based feature selec

78 works reconstructed by the RTN package, gene expression data, and a two-tailed Gene Set Enrichment An

79 d analyze their omics data, such as the gene-expression data, and overlay curated or experimental gen

80 ned using immunostaining, publicly available expression data, and reverse transcriptase quantitative

81 included Independent Sample t-tests for gene expression data, and supervised multi-variate analysis u

82 ically relevant genes, integrate pathway and expression data, and yield more reproducible results acr

83 to organisms for which extensive histone and expression data are available, and does not explicitly i

84 ccount for the nonhuman component of PDX RNA expression data are critical to its interpretation.

85 The currently available P. patens expression data are distributed across three tools with

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

87 ds for functional inference from comparative expression data are lacking.

88 of high-dimensional data (for instance, gene expression data) are ubiquitous in modern bioinformatics

89 ed approach that takes high-dimensional gene expression data as input and performs non-linear mapping

90 method for obtaining spatially resolved gene expression data at resolutions comparable to the sizes o

91 StanDep clusters gene expression data based on their expression pattern across

92 work has been successful in analysis of gene expression data, but application to epigenetic data has

93 embedding of environmental factors from gene expression data by using latent variable (LV) analysis a

94 stic maturation, and that cell type-specific expression data can be extracted using only sequencing r

95 accurately predicted, the corresponding gene expression data can be reliably used in downstream analy

96 tegrate these findings with genome-wide gene expression data collected from the same human liver tiss

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

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

99 t technical and biological artifacts in gene expression data confound commonly used network reconstru

100 sets including both DNA methylation and gene expression data demonstrate favorable performance of the

101 Our Western blotting and messenger RNA expression data demonstrated that canagliflozin augments

102 e by reanalyzing available enhancer and gene expression data derived from ependymoma brain tumors.

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

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

105 non-model species where rich sources of gene expression data exist, but annotation rates are poor and

106 With time-series gene expression data, exploiting inherent time information an

107 vised compression algorithms applied to gene expression data extract latent or hidden signals represe

108 ds for inferring gene-gene interactions from expression data focus on intracellular interactions.

109 Using an encoding for gene expression data, followed by deep neural networks analys

110 RMs based on TF-gene binding events and gene expression data for a particular cell type.

111 genetic analysis and publicly available gene expression data for each gene family, which will aid in

112 Here, we used expression data for mRNAs and microRNAs (miRNAs) from Th

113 (PEATmoss), that unifies publicly available expression data for P. patens and provides multiple visu

114 ructure; direct access to the wild-type gene expression data for the tissues affected in a specific m

115 The expression data for these seven cell populations, coveri

116 ttle, we analysed genome-wide mRNA and miRNA expression data from 114 muscle samples.

117 Analysis of gene-expression data from 159 metastatic CRPC samples and 214

118 ed tool, PathTracer, is demonstrated on gene expression data from 1952 invasive breast cancer samples

119 Expression data from 23 genome-wide platforms were caref

120 Using patients' tumor gene expression data from 4 independent data sets, we correla

121 etwork analysis of sputum cell transcriptome expression data from 84 subjects with asthma and 27 heal

122 lyze paired chromatin accessibility and gene expression data from a retinoic acid (RA)-induced mouse

123 al network-based survival model was built on expression data from a selection of genes most affected

124 Gene expression data from approximately 10,000 tumor samples

125 A metaanalysis of gene expression data from B-ALL patient specimens revealed th

126 Gene expression data from beta-NGF stimulated cartilage callu

127 of these 36 signals were intronic variants; expression data from blood and lung tissue showed that t

128 We apply our approach to gene expression data from both synthetic and real (breast and

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

130 effectively integrated metabolomics and gene expression data from breast cancer mouse models through

131 Here, we perform a new analysis on gene expression data from cells in bronchoalveolar lavage flu

132 ormation from published literature with gene expression data from diverse model systems to infer a se

133 Expression data from diverse organs showed that maize gl

134 to growth rates by gathering published gene-expression data from Escherichia coli and Saccharomyces

135 nomic copy number variations (CNVs) and gene expression data from healthy subjects.

136 Computational approaches that use expression data from heterogeneous samples are promising

137 lished data sets GSE75214 and GDS2642 of RNA expression data from ilea of healthy individuals undergo

138 and detect deleterious expression levels in expression data from individual patients.

139 cytes, and will be useful for analyzing gene expression data from laboratory and field samples.

140 By using a unique set of gene expression data from lung cells obtained using bronchosc

141 s probabilistic classifier was built on mRNA expression data from more than 300 clinical samples of b

142 Analyzing expression data from multiple brain regions from the Gen

143 tween transcriptome and phenome will require expression data from multiple tissues.

144 2 and TMPRSS2 in 2 data sets containing gene expression data from nasal and airway epithelial cells f

145 cially prone to spurious prioritization with expression data from non-trait-related tissues or cell t

146 del (APARENT, APA REgression NeT) on isoform expression data from over 3 million APA reporters.

147 ere externally validated using platelet gene expression data from patients with coronary atherosclero

148 Analysis of gene expression data from patients with neuroblastoma reveale

149 We analyzed clinical, genomic and expression data from patients with oral cavity squamous

150 we build machine learning models using gene expression data from patients' primary tumor tissues to

151 However, expression data from Physcomitrella patens were so far g

152 ional pipeline to analyze publicly available expression data from postmortem brain regions across hum

153 ia-related markers in publicly available RNA expression data from postmortem brain tissue.

154 Gene expression data from pretreatment biopsies of patients w

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

156 When compared with whole blood gene expression data from seven external datasets containing

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

158 TIH3 in the developing human brain using the expression data from the Allen Institute for Brain Scien

159 tering as demonstrated on simulated and real expression data from The Cancer Genome Atlas (TCGA).

160 Mutation, copy number alteration and gene expression data from The Cancer Genome Atlas project wer

161 tic prediction of 15 cancer types using gene expression data from The Cancer Genome Atlas, as well as

162 scale pharmacogenomics study with basal gene expression data from the CCLE project and prior knowledg

163 at distinguish the profiles of the exon-only expression data from the combined exon and intron data.

164 Taken together, gene expression data from the de-novo transcriptome of I. ech

165 Moreover, expression data from the GTEx database demonstrated that

166 This method was tested on microarray expression data from the M3D database, corresponding to

167 nd analyzed the clinical and microarray gene expression data from those individuals to understand var

168 We compress gene expression data from three large datasets consisting of

169 cly available ChIP-seq experiments with gene-expression data from tissue-specific RNA-seq experiments

170 Here, we generated single-cell mRNA expression data from wild-type, DNMT3A, DNMT3A/3B and DN

171 0 genotyped individuals with associated gene expression data from ~51,000 experiments, yielding genet

172 Integrating these mutations with expression data further improves the accuracy of the rec

173 ew datasets include Tabula Muris single-cell expression data, GeneHancer regulatory annotations, The

174 ased robust and superior performance on gene expression data generated by microarray, bulk RNA-Seq an

175 tures and have limited power when applied to expression data generated by RNA-Sequencing (RNA-Seq), e

176 Here, we leveraged mRNA expression data generated from nearly 12,000 human speci

177 ggregation and visualization of differential expression data have discrete functionality and require

178 Our analysis shows that expression data helps resolve the ambiguities arising in

179 The biclustering of large-scale gene expression data holds promising potential for detecting

180 Analysis of gene expression data identified biomarkers associated with re

181 tatistics of kidney function traits and gene expression data identified relevant tissues and cell typ

182 assessment of multimodality in zero-enriched expression data, (ii) a fast and efficient dropouts-savi

183 reviously obtained through longitudinal gene expression data, implying that differential treatment de

184 platform for the targeted quantification of expression data in biofluids and tissues.

185 applied to pancreatic islet and whole kidney expression data in human, mouse, and rats, MuSiC outperf

186 on network-based functional analysis of gene expression data in the hippocampal subfields, CA1 and CA

187 t exhibits properties reflected in real gene expression data, including main effects and network inte

188 Multidimensional scaling analysis of the RNA expression data indicated separate clustering of patient

189 NCS L1000 dataset contains numerous cellular expression data induced by large sets of perturbagens.

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

191 transforming normative bulk-tissue cortical expression data into cell-type expression maps, we link

192 ne regulatory networks from time series gene-expression data is a challenging problem, not only becau

193 Time-course gene expression data is a rich source of information that can

194 In these studies, the gene expression data is either considered at one time-point (

195 that the dynamic nature of time-series gene expression data is more informative in predicting clinic

196 sed on distance alone when extensive histone/expression data is not available for the organism.

197 Sample classification of gene expression data is one such popular bio-data analysis te

198 ta sets illustrating the integration of gene expression data, lipid concentrations and methylation le

199 arge number of irrelevant/redundant genes in expression data makes a sample classification algorithm

200 ew dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23

201 t outputs biomarker values from a normalized expression data matrix.

202 understand cell type composition using gene expression data, methods of estimating (deconvolving) ce

203 erent cancer types using clinical data, mRNA expression data, microRNA expression data and histopatho

204 Our analysis of genome-wide expression data moves us closer to understanding the mol

205 t gene filtering methods based on microarray expression data obtained from a high-quality patient PAH

206 We obtained time-course expression data of a PERIANTHIA (PAN) inducible line and

207 Gene expression data of breast tumors and normal tissues in t

208 lly estimate cell-type proportions from gene expression data of bulk blood samples, but their perform

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

210 We analyzed the expression data of liver tissues from 216 patients with

211 then synthesize high-level trends present in expression data of other archaeal species.

212 mutation data, DNA methylation data and gene expression data of three cancer types from The Cancer Ge

213 orks generated from publicly available tumor expression data onto ChIP-seq data.

214 The availability of high-throughput spatial expression data opens the door to methods that can infer

215 nciles the level of ITH and CTC-derived gene expression data outperformed the initial classifier in p

216 rctic clam (Laternula elliptica) mantle gene expression data produced over an age-categorized shell d

217 Co-directionality of expression data provide new mechanistic insights that ar

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

219 We applied SArKS to published gene expression data representing distinct neocortical neuron

220 s of single-cell and Allen Human Brain Atlas expression data reveal somatostatin interneurons and ast

221 Integration with chondrogenesis gene expression data revealed an enrichment of significant Cp

222 Comparison with RNA-seq expression data reveals a strong overlap between highly

223 for the analysis of time series single cell expression data (scRNA-Seq) either do not utilize inform

224 clude: improvements to the Differential Gene Expression Data Search, facilitating searches for genes

225 w how to use this package to simulate a gene expression data set and consequently benchmark analysis

226 Applying ARTDeco to a gene expression data set from IAV-infected monocytes from dif

227 trong disease signals in an independent gene expression data set of COPD and IPF lung tissue and show

228 The Wolffia genome and TOD expression data set thus provide insight into the interp

229 Gene expression data sets of Multiple tissues and Yeast from

230 ed the vast array of publicly available gene expression data sets to query similarity to CCNB1, which

231 e the method with applications to two global expression data sets, one from the model eukaryote Sacch

232 ach to toxicogenomics and schizophrenia gene expression data sets.

233 dant marker genes from high dimensional gene expression data sets.

234 arvalbumin (PV)-positive neurons; theory and expression data show this is consistent with ISN operati

235 ome-wide association studies data and cancer expression data showed that deTS could effectively ident

236 The public expression data showed that ITIH3 may have a role in the

237 s of chromatin accessibility and single-cell expression data shows that regulatory elements gradually

238 of the human secretome, including body-wide expression data, spatial localization data down to the s

239 Transmission EM as well as host and symbiont expression data suggest that Ca R. santandreae largely p

240 Our gene expression data suggest that malR expression is represse

241 ction of chondrocyte calcification, and gene expression data suggested that the Indian Hedgehog-parat

242 Differential gene expression data suggested the presence of extensive mito

243 Not only did genetic and expression data support a co-factorial relationship, but

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

245 Our gene expression data supports this hypothesis and low E2F2 ac

246 y available scRNA-seq datasets and simulated expression data that bayNorm allows robust imputation of

247 zing H2A.Z occupancy in conjunction with RNA expression data that captures promoter-derived antisense

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

249 the weighted models in the Alzheimer's gene expression data, the number of DE genes decreased in all

250 Applied to gene expression data, the test reveals that the strength of s

251 approach used for selection of thresholds on expression data to consider the associated gene as "acti

252 ion of pathway network information with gene expression data to determine the degree to which a gene

253 nsitivity analysis) to existing hepatic gene expression data to determine the role of transcription f

254 ed RNA deconvolution approach to single-cell expression data to estimate the relative neuronal maturi

255 usly published in vivo in situ hybridization expression data to ground truth gene interactions predic

256 an algorithm that can mine differential gene expression data to identify candidate cell type-specific

257 ohort analysis of publicly available PAH RNA expression data to identify clinically relevant BMPR2 mo

258 gence with brain tissue and single cell gene expression data to identify tissues and cell types assoc

259 or, that combines network analyses with gene expression data to identify transcription factors (TFs)

260 We utilized in vitro E2F2 ChIP-chip and over expression data to identify transcriptional targets of E

261 S summary statistics with summary-level gene expression data to infer differential gene expression in

262 of tuxnet when using different types of gene expression data to infer networks and its accessibility

263 When applied to single-cell gene expression data to investigate mouse medulloblastoma, th

264 The integration of gene expression data to predict systemic lupus erythematosus

265 ove interpretation and power, by aggregating expression data to the pathway level.

266 infer two gene networks separately from gene expression data under two different conditions, and then

267 information as a graph and combines it with expression data using supervised training.

268 nology developed for linear modeling of gene expression data was used in combination with thermally a

269 Using expression data, we confirmed that many epivariations ar

270 Also, incorporating gene expression data, we define a topic activity score that m

271 ing chromatin structure information and gene expression data, we find evidence for venom gene-specifi

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

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

274 By analyzing published gene expression data, we found that the dynamics produced by

275 high-dimensional single-cell protein and RNA expression data, we identified distinct markers to delin

276 onstruction of the parasite to interpret the expression data, we identified targetable pathways in re

277 Coupled with gene expression data, we identify a candidate male-sterility

278 Applying GeneSurrounder to real expression data, we show that our method is able to iden

279 follow-up experiments for longitudinal gene expression data, weather pattern changes over time, and

280 Lean and obese mouse islet expression data were analyzed by weighted gene co-expres

281 Normalized gene expression data were clustered and used as the input fea

282 PSC expression data were compared with transcriptome data of

283 Gene expression data were generated using Affymetrix HG-U133P

284 oth cases, potencies derived from multi-gene expression data were highly correlated with orthogonal p

285 Pairwise interregional correlations in FOS expression data were used to construct network models th

286 e to generate large-scale, multi-tissue gene expression data, where expression profiles are obtained

287 rich resources for tissue-level (bulk) gene expression data, which have been collected from thousand

288 ed solution for the analysis of differential expression data with a rapid turnaround time.

289 tome-wide association studies integrate gene expression data with common risk variation to identify g

290 gned repeated sampling of human sperm sncRNA expression data with concurrent measures of perceived st

291 sed co-expression modules inferred from gene expression data with five methods as traits in trans-eQT

292 context, which is necessary for overlapping expression data with other datasets.

293 that statistically sound combination of gene expression data with prior knowledge about biology in th

294 al modeling to integrate time series of mRNA expression data with sleep-wake history, which establish

295 atics approach, which connects the HSPC gene expression data with the candidate cargo in stimulatory

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

297 The combination of GPCR expression data with validation studies (e.g., signaling

298 nfer gene networks and perform MRA from gene expression data, with optional corrections for copy-numb

299 Concordant patterns are apparent in gene expression data, with regional differentiation following

300 Integrating methylation and expression data within a Mendelian randomisation framewo