ライフサイエンスコーパス: gene interaction

1 erarchical protein domain similarity or gene-gene interactions).

2 s the likely cause of the loss of VDR-target gene interaction.

3 his pathway show replicable evidence of gene-gene interaction.

4 rt a site specificity model of LCR HS-globin gene interaction.

5 es upon prior methods that do not utilize TF-gene interaction.

6 risk of MetS independently and through gene-gene interactions.

7 more than doubled the overall count of drug-gene interactions.

8 racial ancestry, gene-environment, and gene-gene interactions.

9 ional approach for predicting distal element-gene interactions.

10 work definitions with yes/no labels for gene-gene interactions.

11 unknown causal variants to find distant gene-gene interactions.

12 collection of previously characterized gene-gene interactions.

13 ing that FOXP3 may play a role in long-range gene interactions.

14 ithms, some of which utilized information on gene interactions.

15 environments by changing gene expression and gene interactions.

16 elationships due to gene-environment or gene-gene interactions.

17 l gene expression as opposed to differential gene interactions.

18 to account the potentially diverse nature of gene interactions.

19 ing task, especially in the presence of gene-gene interactions.

20 nthetic rescues previously observed for gene-gene interactions.

21 genetic complexity of NTDs and critical gene-gene interactions.

22 dels, and is able to capture high-order gene-gene interactions.

23 I makes novel and interesting predictions of gene interactions.

24 eatments might be influenced by complex gene-gene interactions.

25 ofiles to identify True REGulatory (TREG) TF-gene interactions.

26 organism for investigating the developmental gene interactions.

27 data with no prior knowledge of pathways or gene interactions.

28 the likelihood of True REGulatory (TREG) TF-gene interactions.

29 unctionally redundant genes and of epistatic gene interactions.

30 ences in severity are the result of multiple gene interactions.

31 ity among cancers through their shared miRNA-gene interactions.

32 ies may be more fruitful in identifying diet-gene interactions.

33 ncers to improve the identification of miRNA-gene interactions.

34 esent targetHub, a CouchDB database of miRNA-gene interactions.

35 ons of polytopes with qualitative aspects of gene interactions.

36 ersity, through both single gene effects and gene interactions.

37 ble approach based on boosting to study gene-gene interactions.

38 predicted miRNA-gene interactions, and gene-gene interactions.

39 ects of each individual gene as well as gene-gene interactions.

40 an also arise from aberrations in these gene-gene interactions.

41 erage of the underlying network of regulator-gene interactions.

42 rmatics databases, and identified known drug-gene interactions.

43 ogical properties, outlining known and novel gene interactions.

44 CODE on transcription factor (TF) and target gene interactions.

45 tackle complex covariance structures of gene-gene interactions.

46 ug-hERG channel (human Ether-a-go-go-Related Gene) interactions.

47 ling to highlight conserved and differential gene interactions across experimental conditions, withou

48 one to describe how individual genes or gene-gene interactions affect classification decisions.

49 Many studies have attempted to identify gene-gene interactions affecting asthma susceptibility.

50 The study identified gene-gene interactions among SPRY genes among 1,908 NSCL/P tr

51 We develop C++ software for genome-wide gene-gene interaction analyses (GWGGI).

52 Gene-by-gene interaction analyses suggested that the presence of

53 Gene-gene interaction analyses yielded 10 pairs of SNPs in Eu

54 Enrichment, Sub-Network Enrichment, and Gene-Gene Interaction analyses, with 4 metrics proposed and v

55 d a risk score model was applied for gene-by-gene interaction analyses.

56 A subsequent drug-gene interaction analysis identified a pharmacogenomic a

57 Gene-by-gene interaction analysis suggests that FCER2 polymorphi

58 y, we demonstrated that the genome-wide gene-gene interaction analysis using GWGGI could be accomplis

59 iables predictive of the outcome, and a gene-gene interaction analysis was carried out.

60 rolling certain genome operations, including gene interaction and gene regulation.

61 xternal sources of orthology, gene ontology, gene interaction and pathway information.

62 To gain valuable insights into the gene interaction and the complex regulation system invol

63 ancestral block, our LABST exploits ancestry-gene interaction and the number of rare alleles therein.

64 es high confidence transcription factor (TF)-gene interactions and annotates the interactions with in

65 or-lncRNA interactions, transcription factor-gene interactions and construction of a context-specific

66 Our theory applies to all common types of gene interactions and facilitates comprehensive investig

67 lar organisms requires detailed knowledge of gene interactions and gene expressions.

68 ormation about the network, e.g. gene lists, gene interactions and gene functional annotations.

69 erogeneity and use it to identify functional gene interactions and genotype-dependent liabilities in

70 pathways, analyzing upstream and downstream gene interactions and integrating non-coding regions tha

71 lists of genes against a compendium of drug-gene interactions and potentially 'druggable' genes.

72 ions to facilitate necessary long-range gene-gene interactions and regulations.

73 om transcriptomic studies can elucidate gene-gene interactions and regulatory mechanisms.

74 es should accelerate studies of complex gene-gene interactions and screening of new drug targets.

75 ble Gene Ontology annotations of genes, gene-gene interactions and the relations between genes and is

76 expands the known repertoire of TF-cytokine gene interactions and the set of TFs known to regulate c

77 We devised an approach to select robust TF-gene interactions and to determine localized contributio

78 impact of individual insulators on enhancer-gene interactions and transcription remains poorly under

79 cellular processes in order to capture multi-gene interactions and yield mechanistically interpretabl

80 cs data are then used to assign penalties to genes, interactions and pathways.

81 related concepts based on drug-gene and gene-gene interactions) and two extrinsic evaluations (protei

82 oduct interaction, overexpression phenotype, gene interaction, and gene structure correction.

83 this complexity are epistasis, meaning gene-gene interaction, and pleiotropy, in which one gene affe

84 ure of underlying biological traits, such as gene interactions, and (b) the need for highly interpret

85 o account worldwide allele frequencies, gene-gene interactions, and contrasted situations of environm

86 nowledge of the molecular mechanisms, genes, gene interactions, and gene regulation governing the dev

87 cer samples, computationally predicted miRNA-gene interactions, and gene-gene interactions.

88 genetic structure, complex haplotypes, gene-gene interactions, and rare variants to detect and repli

89 Long-range gene-gene interactions are biologically compelling models for

90 Chromatin organization and gene-gene interactions are critical components of carrying ou

91 However, complicated etiologies such as gene-gene interactions are ignored by the univariate analysis

92 Gene-gene interactions are of potential biological and medica

93 tion for hidden structure is needed, or gene-gene interactions are sought, state-of-the art algorithm

94 Gene-gene interactions are susceptible to the same problem if

95 neighborhoods provide for specific enhancer-gene interactions, are essential for both normal gene ac

96 might facilitate the identification of gene-gene interactions associated with AF.

97 FA-APOE gene interactions at baseline and following change in pl

98 relation is popularly used to elucidate gene-gene interactions at the whole-genome scale, many compli

99 we develop a Benchmark of candidate Enhancer-Gene Interactions (BENGI) by integrating the recently de

100 ariate analyses were used to identify a gene-gene interaction between ADRB2 gene and each of these 4

101 We tested for gene-gene interaction between AXIN2 and additional cleft susc

102 In addition, a strong gene-gene interaction between homer 1 homolog (Drosophila) (H

103 We looked for evidence of gene-gene interaction between IRF6 and TGFA by testing if mar

104 Notably, there was gene-gene interaction between TSLP and IL4 SNPs (P = .0074).

105 We detected and confirmed gene-gene interactions between the HLA region and CTLA4, IRF5

106 In this study, we explored FA-gene interactions between the missense APOE polymorphism

107 data implies coregulation and potential gene-gene interactions, but provide little information about

108 In this work, we elucidate higher level gene-gene interactions by evaluating the conditional dependen

109 In conclusion, exploiting ancestry-gene interaction can boost statistical power for rare va

110 pes of deleterious alleles and thus ancestry-gene interaction can influence disease risk in the admix

111 etworks, which may aid the discovery of gene-gene interactions changed under different conditions.

112 we identify a large sample of published drug-gene interaction claims curated in the Comparative Toxic

113 and 4253 opposing findings about 51,292 drug-gene interaction claims in 3363 scientific articles.

114 and test verification, and to identify gene-gene interactions collected from the GeneFriends and DIP

115 nd likely multigenic trait for which complex gene interactions come into play.

116 N-regulatory network using transcriptome and gene interaction data from Arabidopsis and new data from

117 gets, there are few tools to integrate miRNA-gene interaction data into high-throughput genomic analy

118 of biotechnology techniques that can provide gene interaction data on a large, possibly genomic scale

119 ta, and overlay curated or experimental gene-gene interaction data to extend pathway knowledge.

120 icting new aging-related drugs based on drug-gene interaction data.

121 twork based solely on rice transcriptome and gene interaction data.

122 The Drug-Gene Interaction database (DGIdb) mines existing resourc

123 A Drug Gene Interaction Database search identified 47 gene prod

124 e collection of a comprehensive set of known gene interactions derived from a variety of publicly ava

125 ns of DDIs and ADR types by integrating drug-gene interactions (DGIs).

126 epistasis may differ for within- and between-gene interactions during adaptation and that diminishing

127 d are extremely useful for identifying local gene interactions during normal development.

128 es or correlation changes of individual gene-gene interactions, EDDY compares two conditions by evalu

129 variable importance measures to detect gene-gene interaction effects and their potential effectivene

130 ts offer a new path to the detection of gene-gene interaction effects.

131 n takes advantage of prior knowledge of gene-gene interactions, encouraging pairs of genes with known

132 a quantitative index called Differentiation Genes' Interaction Enrichment (DGIE) is presented to qua

133 ty method that blends the importance of gene-gene interactions (epistasis) and main effects of genes.

134 ces of sex-dependent genetic effects or gene-gene interactions (epistasis).

135 MFR is used to predict gene-gene interactions extracted from the COXPRESdb, KEGG, HP

136 sults demonstrate the advantage of combining gene interactions extracted from the literature in the f

137 thetic lethality (SL) is a promising form of gene interaction for cancer therapy, as it is able to id

138 ch revealed the importance of potential gene-gene interactions for understanding the genetic architec

139 cal and estrogenic responses, known chemical:gene interactions from biological pathways and knowledge

140 The server also supports predicted miRNA:gene interactions from DIANA-microT-CDS for 4 species (h

141 Most methods for inferring gene-gene interactions from expression data focus on intracel

142 However, analytical tools for discovering gene interactions from such data remain an open challeng

143 ully extract significantly dysregulated gene-gene interactions from the data.

144 a vital role in biological processes such as gene interaction, gene regulation, DNA replication and g

145 risk, the ability to detect significant gene-gene interaction (GGI) effects has been limited to date.

146 new genes must integrate into ancestral gene-gene interaction (GGI) networks.

147 Here we examined whether gene-gene interactions had any roles in regulating SUA using

148 ut their ability to accurately simulate gene-gene interactions has not been investigated extensively.

149 However, detecting gene-gene interactions has proven to be very difficult due to

150 Specifically, nine new sources of drug-gene interactions have been added, including seven resou

151 Gene-gene interactions have been increasingly regarded as con

152 Gene-gene interactions have been proposed as a source to expl

153 e major genes involved in classical gene-for-gene interactions have now been identified, and breeders

154 uch as BioGRID and ChEA, annotate these gene-gene interactions; however, curation becomes difficult a

155 Global analysis of miRNA-target gene interactions identified two sub-network modules, th

156 We exploited CDSM to elucidate gene interactions important for cellular processes poorl

157 uggestive evidence of replication for a gene-gene interaction in asthma involving loci that are poten

158 Testing gene-gene interaction in genome-wide association studies gene

159 aditional dosage method to detection of gene-gene interaction in terms of power while providing contr

160 A gene-gene interaction in the T1D data were observed between t

161 of measuring similarity between profiles of gene interactions in a cell.

162 The aim of this study was to test for gene-gene interactions in a number of known lupus susceptibil

163 ise unsuccessful GWAS data, to identify gene-gene interactions in a way that enhances statistical pow

164 bility of our method in (i) identifying gene-gene interactions in autophagy-dependent response to Sal

165 l process, are important models for studying gene interactions in complex tissues.

166 R-FC were determined by assessing treatment-gene interactions in Cox proportional hazards models.

167 reat need for the identification of new gene-gene interactions in high-dimensional association studie

168 While the importance of gene-gene interactions in human diseases has been well recogn

169 Participants in the NUGENOB (Nutrient-Gene Interactions in Human Obesity) trial consumed a hyp

170 PCR and clustering analysis, we studied gene-gene interactions in human skeletal muscle and renal epi

171 re have been few definitive examples of gene-gene interactions in humans.

172 , we applied this strategy to identify novel gene interactions in KRAS-mutant cancer cells.

173 tudying fundamental biological processes and gene interactions in non-model species where rich source

174 ork score that explicitly considers pairwise gene interactions in PPI networks, and it searches for s

175 nder curve (AUC) values for identifying gene-gene interactions in the development, test, and DIP data

176 The role of gene interactions in the evolutionary process has long b

177 recombinational suppressors and/or epistatic gene interactions in the MAT-CEN intervening regions.

178 ss of resource limitation increased positive gene interactions in the MBR system.

179 A subset of 10 core genes and gene-gene interactions in the network module were validated b

180 een clusters, which in turn maximizes global gene interactions in the nucleolus.

181 Our findings implicate diet-gene interactions in the pathogenesis of CD.

182 a striking pattern of miRNA-tumor suppressor gene interactions in this cancer.

183 The evidence of gene-gene interactions in this study also provided clues for

184 These gene-gene interactions include both protein-protein interacti

185 e met in the problems involved in estimating gene interactions, inferring causality and modeling temp

186 Identifying gene-gene interaction is a hot topic in genome wide associati

187 r data showed that this alternative layer of gene interaction is essential in global information flow

188 Gene-by-gene interaction is one important potential source of un

189 A complete repository of gene-gene interactions is key for understanding cellular proc

190 erefore depends on genotypic context through gene interactions known as epistasis.

191 am subnetwork from a super-network including gene interactions known to occur under various molecular

192 la heart as a platform for identifying novel gene interactions leading to heart disease, we found tha

193 these results show that the inverse gene-for-gene interactions leading to necrotrophic effector-trigg

194 We present a global gene interaction map of the human heart failure transiti

195 ute to human craniofacial defects, this gene-gene interaction may have implications on craniofacial d

196 hese results suggest that understanding gene-gene interactions may be important in resolving Alzheime

197 elling--than has been presumed; selection on gene interactions may entail the maintenance of genetic

198 ene Ontology (GO) annotations, pathways, and gene interaction modules.

199 enrichment), as is the calmodulin 1 (CALM1) gene interaction network (P </= 4.16E-04, 14.4-fold enri

200 TFBS) and microRNA target data to generate a gene interaction network across 28 human tissues.

201 nge chromatin conformation data identified a gene interaction network dominated by NKX2-5, TBX3, ZFHX

202 the underlying regulatory pathways within a gene interaction network is a fundamental problem in Sys

203 We created a gene-gene interaction network of the conserved molecular feat

204 elationship between these genes using a gene-gene interaction network, and place the genetic risk loc

205 opsis thaliana seeds to compute a functional gene interaction network, termed Seed Co-Prediction Netw

206 sion correlation (MEC), and methods that use gene interaction network-based analysis (INA).

207 al gene's synergistic influence in a gene-to-gene interaction network.

208 cal pathways, Gene Ontology (GO) terms, gene-gene interaction networks (importantly, with the directi

209 ggesting that outcomes may be constrained by gene interaction networks [1].

210 INI, a machine learning system for inferring gene interaction networks from Drosophila embryonic ISH

211 istically sound, and biologically meaningful gene interaction networks from image data.

212 ortunities to gain a deeper understanding of gene interaction networks that provide organismal form a

213 sing prior biological knowledge expressed as gene interaction networks to guide the search for associ

214 e consequently present an approach to define gene interaction networks underlying important cellular

215 work for quantitative assessment of inferred gene interaction networks using knock-down data from cel

216 We propose that modifier effects emerge from gene interaction networks whose structure and function v

217 Although these methods use static gene interaction networks, functional clustering of gene

218 g HIT'nDRIVE-seeded driver gene modules from gene interaction networks.

219 ved gene lists in the context of large-scale gene interaction networks.

220 n, navigation, visualization and analysis of gene interaction networks.

221 In addition, we describe robust gene-interaction networks recapitulating both protein co

222 is fundamentally different from the gene-for-gene interaction of host-pathogen coevolution.

223 ality reduction in the PIAMA study, and gene-gene interactions of 10 SNP pairs were further evaluated

224 to ASD, possibly caused by nonadditive gene-gene interactions of shared risk loci.

225 f our study is to examine the effect of gene-gene interaction on AF susceptibility.

226 admixed individuals to find signals of gene-gene interaction on human traits and diseases.

227 and describe a novel method for testing gene-gene interaction on marginally imputed values of untyped

228 , to date, formal statistical tests for gene-gene interaction on untyped SNPs have not been thoroughl

229 e, and there have been few searches for gene-gene interactions on a genome-wide scale.

230 The effects of statistical gene-gene interactions on phenotypes have been used to assign

231 e gene expression data to infer a network of gene interactions on the basis of their correlated respo

232 ks such as protein-protein interaction, gene-gene interaction or any other correlation or coexpressio

233 two genes simultaneously to discover complex gene interactions or to distinguish between direct and i

234 blets are helpless for discovering pair-wise gene interactions, our approach can discover typical pai

235 e, in many such cases, the expression of the gene interaction partners (gene 'interactome') correlate

236 ve a variety of context-specific protein and gene interaction partners, and also modulate gibberellin

237 Then, we studied collective gene interaction patterns and uncovered highly interwove

238 Furthermore, we demonstrated that gene x gene interactions play important roles in the formation

239 s epigenetic data to estimate regulatory and gene interaction potential, and identifies combination o

240 ybridization expression data to ground truth gene interactions predicted by the GRN and show that can

241 y for our reference list of synthetic lethal gene interactions (R = 0.159).

242 unprecedented detail, but also identify new gene interactions responsible for inner ear development

243 Gene-gene interactions shape complex phenotypes and modify th

244 rks against manually curated databases of TF-gene interactions show that our method can accurately de

245 at local ancestry can be used to detect gene-gene interactions, solving the computational bottleneck.

246 t closely-related first-degree neighbours in gene interaction space.

247 ession according to the regulatory logic and gene interactions specified in a GRN model for embryonic

248 in the etiology of NSCL/P by detecting gene-gene interactions: SPRY1, SPRY2, and SPRY4-with SPRY3 ex

249 pplications, including protein localisation, gene interaction studies and high-throughput genetic scr

250 We aimed to conduct a genome-wide gene-gene interaction study for asthma, using data from the G

251 ia coli computes one particular type of gene-gene interaction, synthetic lethality, and find that the

252 covariate x environment and the covariate x gene interaction terms in the same model that tests the

253 The key concern for gene-gene interaction testing on untyped SNPs located on diff

254 d kernel changes with each test, as for gene-gene interaction tests.

255 ification of the likelihood of regulatory TF-gene interaction that can be used to either identify reg

256 IN project, demonstrating an example of gene-gene interaction that plays a role in the largely unchar

257 iles from multiple cancers to identify miRNA-gene interactions that are both common across cancers an

258 gation analysis results in identifying miRNA-gene interactions that are mostly common across datasets

259 ystrophy is one approach to deciphering gene-gene interactions that can be exploited for therapy deve

260 o the complexity of the genetic cascades and gene interactions that determine the evolutionary patter

261 monstrate theoretically that the presence of gene interactions that favor coadaptation can also favor

262 We built an extractor for gene-gene interactions that identified candidate gene-gene re

263 ter understanding of quantitative trait loci/gene interactions that influence fiber quality and yield

264 del to investigate how natural selection and gene interactions (that is, epistasis) shape the evoluti

265 lifetime that promotes pairwise and cluster gene interactions through "flexible" clustering.

266 ed on correlation changes of individual gene-gene interactions, thus providing more informative resul

267 contribution, within the complex network of gene interactions, to the cellular processes responsible

268 alable strategy for inferring multiple human gene interaction types that takes advantage of data from

269 approaches are usually unable to detect gene-gene interactions underlying complex diseases.

270 all pairs of genes to detect pairwise gene x gene interactions underlying disease.

271 ted markers in 3 SPRY genes to test for gene-gene interactions using 1,908 case-parent trios recruite

272 hat the contribution to heritability of gene-gene interactions varies among traits, from near zero to

273 issue for translational genomics is to model gene interaction via gene regulatory networks (GRNs) and

274 odels was applied to test for potential gene-gene interactions via the statistical package TRIO in R

275 exploratory analysis, a significant nutrient-gene interaction was apparent when baseline plasma selen

276 A nutrient-gene interaction was observed when the baseline selenium

277 Using a novel method to delineate enhancer-gene interactions, we show that multiple enhancer varian

278 Eight additional gene-gene interactions were also marginally significant (P <

279 Gene-by-gene interactions were also suggested, where the combine

280 Clinical impact and gene interactions were analysed using TCGA database.

281 Gene-gene interactions were assessed through model-based mult

282 Such gene-gene interactions were especially pronounced for NPM1-mu

283 A total of 28 three-way gene interactions were identified, suggesting the existe

284 Gene-gene interactions were tested and for comparison purpose

285 ive tools to make inferences about epistatic gene interactions when the fitness landscape is only inc

286 d used to increase power when assessing gene-gene interactions, which requires a test for interaction

287 ism by which E2F and Cabut regulate distinct gene interactions, while still sharing a small core netw

288 The characterization of miRNA-gene interactions will lead to a better understanding of

289 exposures, as opposed to major APOL1-second gene interactions, will prove to be stronger modifiers o

290 first insect hypothesized to have a gene-for-gene interaction with its host plant, wheat (Triticum sp

291 not support a gene-gating model in which GAL gene interaction with the nuclear pore ensures rapid gen

292 d a large-scale association analysis of gene-gene interactions with AF in 8,173 AF cases, and 65,237

293 he Web site provides RESTful access to miRNA-gene interactions with an assortment of gene and miRNA i

294 s further revealed potential high-order gene-gene interactions, with VEGFC: rs3775194 being the initi

295 two limiting cases each with far less global gene interactions: with shorter crosslink lifetimes, "ri

296 indicate that the position and properties of gene interactions within a network can have important ev

297 romoters, as well as the role of 'redundant' gene interactions within regulatory networks.

298 throughput sequencing studies place enhancer-gene interactions within the 3D context of chromosome fo

299 ntly analyze multiple cancers to study miRNA-gene interactions without combining all the data into on

300 A new study demonstrates a strong diet-gene interaction: worms with reduced nhr-114 activity ar