ライフサイエンスコーパス: gene interaction

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

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

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

4 ults clearly demonstrate tissue-specific FXR/gene interaction.

5 in ROBO1 and obesity and hypothesized an age-gene interaction.

6 environments by changing gene expression and gene interactions.

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

8 to account the potentially diverse nature of gene interactions.

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

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

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

12 I makes novel and interesting predictions of gene interactions.

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

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

15 organism for investigating the developmental gene interactions.

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

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

18 unctionally redundant genes and of epistatic gene interactions.

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

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

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

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

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

24 ons of polytopes with qualitative aspects of gene interactions.

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

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

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

28 well understood, in part due to unknown gene-gene interactions.

29 ny phenotypes are the result of complex gene-gene interactions.

30 transcription factor-gene and regulatory RNA-gene interactions.

31 n to assess the potential influences of gene-gene interactions.

32 es the potential importance of micronutrient-gene interactions.

33 gene networks often used to understand gene-gene interactions.

34 scopy, were used to study foxC1 function and gene interactions.

35 We also investigated for gene-gene interactions.

36 eling framework which begins to capture gene-gene interactions.

37 a unique resource for systematic analysis of gene interactions.

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

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

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

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

42 ional approach for predicting distal element-gene interactions.

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

44 tackle complex covariance structures of gene-gene interactions.

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

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

47 avirulence (Avr) proteins through 'gene-for-gene' interactions.

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

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

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

51 Furthermore, we identified gene-gene interaction among the TBX21 and STAT4 variants, suc

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

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

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 n signals and underscore the utility of gene-gene interaction analysis in characterizing the genetic

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

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

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

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

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

63 Understanding gene interaction and pleiotropy are long-standing goals

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

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 sis of these networks provides insights into gene interactions and functions.

68 dates disparate data sources describing drug-gene interactions and gene druggability.

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

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

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

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

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

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

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

76 pression data, particularly in the TF-target gene interactions and the combinatorial nature of gene r

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

78 ype that is a consequence of epistasis (gene-gene interaction) and other phenomena such as gene-envir

79 e role of gene-environment interaction, gene-gene interaction, and epigenetics in food allergy remain

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

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

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

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

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

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

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

87 Because these miRNAs and target gene interactions are conserved, our findings may also b

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

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

90 Gene-gene interactions are of potential biological and medica

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

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

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

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

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

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

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

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

99 pose they illustrate different gradations of gene interaction between complex A proteins themselves a

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

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

102 dient of infection, focusing on the gene-for-gene interaction between the Rpm1 resistance gene in Ara

103 r the two chromosome segments or a result of gene interaction between them.

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

105 h studies pave the way for investigations of gene interactions between pathogen virulence factors and

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

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

108 cally complex, suggesting there may be other genes, interactions between genes, and/or environmental

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

110 We quantify the effect of gene interactions by defining the interaction ratio, CR=

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

112 to improve the ability of MDR to detect gene-gene interactions by replacing classification error with

113 c example of how a virus-plus-susceptibility gene interaction can, in combination with additional env

114 trated as a powerful tool for detecting gene-gene interactions, can be improved with the use of alter

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

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

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

122 The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a we

123 A Drug Gene Interaction Database search identified 47 gene prod

124 te transitive connections that span multiple gene interaction databases.

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

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

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

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

129 usters were also identified when we compared gene interactions during the presence of Rhombic lip ver

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

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

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

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

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

135 We analyzed the gene interactions for two gene data sets (one group is e

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

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

138 creening procedure for the detection of gene-gene interactions from microarray data.

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

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

141 This virus-plus-susceptibility gene interaction generated abnormalities in granule pack

142 ene, environment-gene (epigenetics), and sex-gene interactions, genome-wide association, and whole ge

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

144 For example, differential gene interactions give rise to various stages of morphog

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

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

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

148 Epistasis (i.e. gene-gene interaction) has long been recognized as an importa

149 Epistasis, the presence of gene-gene interactions, has been hypothesized to be at the ro

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

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

152 in this paper that methods considering gene-gene interactions have better classification power in ge

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 enotyped individuals to detect possible gene-gene interactions; (iii) use of high throughput genomic

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 The range of possible gene interactions in a multilocus model of a complex inh

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

180 Many popular methods for exploring gene-gene interactions, including the case-only approach, rel

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

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

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

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

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

186 In GWAS, detecting epistasis (or gene-gene interaction) is preferable over single locus study

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

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

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

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

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

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

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

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

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

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

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

198 ly Bayesian method for reverse engineering a gene interaction network, based on time course data with

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

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

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

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

203 an alternative approach that puts into focus gene interaction networks and molecular pathways rather

204 Gene interaction networks constructed from six independe

205 is measure and in silico evolution we derive gene interaction networks for anterior-posterior (AP) pa

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

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

208 Many online sources of gene interaction networks supply rich visual data regard

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

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

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

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

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

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

215 t common data source for reverse engineering gene interaction networks.

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

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

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

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

220 nges affecting the expression, sequence, and gene interactions of HaFT paralogs have played key roles

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

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

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

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

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

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

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

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

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

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

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

232 ear shorter TTP on ADT, demonstrating a gene-gene interaction (P(interaction) = .041).

233 a binary matrix of transcription factor (TF)-gene interactions partitioning genes into modules and a

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

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

236 luable tool for in silico discovery of novel gene interaction pathways, which can be experimentally t

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

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

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

240 this study, we propose an approach to detect gene interactions related to study phenotypes through id

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

242 Gene-gene interactions shape complex phenotypes and modify th

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

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

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

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

247 Furthermore, gene-gene interaction studies suggest that IRF5, STAT4, and B

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

249 the genes in the signature revealed several gene interactions suggestive of apoptosis as a process p

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

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

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

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

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

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

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

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

258 often involves the evolution of incompatible gene interactions that cause sterility or lethality in h

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

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

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

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

263 the zebrafish to characterize phenotypes and gene interactions that may impact glaucoma pathogenesis.

264 ts that variation of aganglionosis is due to gene interactions that modulate the ability of ENPs to p

265 mple sets of genes, disregarding the complex gene interactions that these pathways are built to descr

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

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

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

269 n a comprehensible graphical format, showing gene interaction type and strength, database source and

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

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

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

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

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

277 ogies triggered by virus-plus-susceptibility gene interaction were dependent on TNFalpha and IFNgamma

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

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

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

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

282 Potential higher order gene-gene interactions were identified, which categorized pat

283 Gene-gene interactions were tested and for comparison purpose

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

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

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

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

288 These results imply that, in general, gene interactions will result in greater heritability of

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

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

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

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

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

294 nt have been made on the genetic control and gene interaction within this structure.

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

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

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

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

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

300 ted, but discovery of their role in gene-for-gene interaction would be novel and needs to be further