ライフサイエンスコーパス: Gene Ontology

1 19,363 were assigned functional categories (gene ontology).

2 icant enrichments independently validated by gene ontology.

3 ations using a panel of ontologies including Gene Ontology.

4 ities between gene products according to the Gene Ontology.

5 investigations of organelles and complement Gene Ontology.

6 term enrichment analysis using NeXO and the gene ontology.

7 with regards to introns, genes, and affected gene ontologies.

8 , and (v) 1,454 gene sets curated from known gene ontologies.

9 d by depth of proteins within hierarchies of gene ontologies.

10 were extracted from the transcriptomes using Gene Ontology, adult-brain gene lists generated by Trans

11 Gene ontology analyses implicated the increasing pattern

12 nt in the large majority of wild strains and gene ontology analyses indicate that several gene catego

13 Gene ontology analyses revealed activation of metabolic

14 ong half-lives, are actively translated, and gene ontology analyses revealed that they are enriched i

15 Ingenuity Pathway Analysis and Gene Ontology analyses suggested that 'cell cycle'-relat

16 le for driving the pathway associations, and gene ontology analysis demonstrated enrichment for calci

17 Furthermore, gene ontology analysis demonstrated that DMRs associated

18 Gene ontology analysis demonstrates that a large portion

19 Gene Ontology analysis demonstrates that HA-CS NPs were

20 Gene ontology analysis found significant enrichment for

21 Gene ontology analysis highlighted a preponderance of ce

22 Gene ontology analysis identified a novel role for Matri

23 ctional overlap revealed through comparative gene ontology analysis in both species.

24 Gene ontology analysis indicated that HHP at 40 and 60 M

25 ntenic genes compared to syntenic genes, and gene ontology analysis indicated that non-syntenic genes

26 Pathway and gene ontology analysis indicated that the upregulated co

27 Gene ontology analysis indicated that their main molecul

28 Gene ontology analysis indicates reduced metabolic proce

29 Gene ontology analysis indicates that the unique distrib

30 Through gene ontology analysis of Akt-regulated PRB target genes

31 Gene ontology analysis of dysregulated PRDM5-target gene

32 Gene ontology analysis of genes activated at the four-ce

33 Gene ontology analysis of genes with DMCs at TSSs reveal

34 Gene ontology analysis of highly represented genes from

35 Gene ontology analysis of mutated genes revealed many bi

36 e the function of mitochondria, according to gene ontology analysis of proteins that are down-regulat

37 Gene ontology analysis of SR1-regulated genes confirmed

38 Gene Ontology analysis of the binding partners revealed

39 A gene ontology analysis of the entire set of differential

40 Gene ontology analysis of the predicted miRNA target gen

41 Gene ontology analysis of the secretory proteins reveale

42 Gene Ontology analysis placed these genes and proteins i

43 Pathway and gene ontology analysis revealed differential expression

44 Gene ontology analysis revealed enrichment in processes

45 Gene ontology analysis revealed enrichment of cell migra

46 Gene ontology analysis revealed enrichment of signaling

47 Gene ontology analysis revealed enrichment of smoking-re

48 Gene ontology analysis revealed that genes whose promote

49 Gene Ontology analysis revealed that nuclear lumen, nucl

50 Gene ontology analysis revealed that PARP could exert th

51 Gene ontology analysis revealed that proteins in numerou

52 Gene Ontology analysis revealed that S-(+)-fipronil caus

53 Gene ontology analysis revealed that the 2,688 Cr-respon

54 A gene ontology analysis reveals that stress response proc

55 Gene ontology analysis showed enrichment for TORC1-regul

56 Gene ontology analysis showed in AKI an association of d

57 Gene ontology analysis showed that components of the ext

58 Gene ontology analysis showed that genes affected by TRA

59 ance and stress fiber formation by TGF-beta, gene ontology analysis showed that genes encoding extrac

60 Gene ontology analysis showed that several pathways regu

61 Gene ontology analysis showed that the cell type-specifi

62 Gene Ontology analysis showed that the targets are broad

63 Gene ontology analysis showed that unique categories in

64 Gene ontology analysis suggests disturbances in gut epit

65 Gene ontology analysis supports our results and gives a

66 Gene Ontology analysis using loci bearing unique GDM- an

67 ap production, principal component analysis, gene ontology analysis, and dynamic network analysis.

68 sion analysis, principal component analysis, gene ontology analysis, and network analysis) or multipl

69 ong the mitochondrial proteins identified by gene ontology analysis, the expression of voltage-depend

70 terized the functional properties of DEGs by Gene Ontology analysis.

71 y relevant genes which are well justified by gene ontology analysis.

72 Gene ontology and biological pathways analyses revealed

73 The annotation with gene ontology and Clusters of Orthologous Group terms ca

74 arch against the Aracyc, Swiss-Prot, TrEMBL, gene ontology and clusters of orthologous groups (KOG) d

75 Gene ontology and enrichment analyses indicated TGFbeta

76 bility to predict biological functions using Gene Ontology and gene-disease associations using Human

77 This hypothesis was corroborated by gene ontology and global interactome analyses, which hig

78 Gene Ontology and KEGG pathway analyses shed light on th

79 involved in the regulation of miR-124-3p by Gene Ontology and Kyoto Encyclopedia of Genes and Genome

80 Gene Ontology and Kyoto Encyclopedia of Genes and Genome

81 notype annotations for mouse genes using the Gene Ontology and Mammalian Phenotype Ontology.

82 Gene ontology and network analyses showed enrichment of

83 Our Gene Ontology and other functional and phenotypic annota

84 BovineMine will be especially useful for gene ontology and pathway analyses in conjunction with G

85 Gene ontology and pathway analyses revealed that many of

86 t into the function of the DEGs, we examined gene ontology and pathway and phenotype enrichment and f

87 t into the function of the DEGs, we examined gene ontology and phenotype enrichment and found signifi

88 Gene Ontology and PubMatrix analyses of differentially e

89 e sets defined typically on the basis of the Gene ontology and the KEGG database of metabolic network

90 a reference-transcriptome-guided approach on gene ontology and tox-pathways, we confirmed the novel a

91 terms are obtained from public sources (the Gene Ontology and UniProt-together containing several th

92 representation and search strategy based on Gene-Ontology and orthogonal non-negative matrix factori

93 many of the identified biological pathways, gene ontologies, and individual genes are associated wit

94 fferentially expressed transcripts, enriched gene ontology, and altered functions and canonical pathw

95 tional annotations for mouse genes using the Gene Ontology, and MGD curates and integrates comprehens

96 model organism databases, consortia such as Gene Ontology, and other databases within NCBI.

97 ethods based on knowledge databases (such as gene ontology annotation (GOA) database) are known to be

98 The Gene Ontology Annotation (GOA) resource provides evidenc

99 Gene ontology annotation and network analysis showed tha

100 ctable in the circulation, we also created a gene ontology annotation for circulating miRNAs using th

101 und information provided by a combination of Gene Ontology annotation information and protein interac

102 e phenotype), and molecular data types (e.g. Gene Ontology Annotation, protein interactions), as well

103 pings using the semantic similarity of their Gene Ontology annotations and observe that L-GRAAL best

104 or extension packages and publicly available gene ontology annotations facilitates straightforward in

105 typic information of individual samples with gene ontology annotations to derive a ranking of genes a

106 external resources and include terms such as Gene Ontology annotations, domains, secondary structure

107 genes/proteins, diseases, taxa, phenotypes, Gene Ontology annotations, pathways and interaction modu

108 ent testing, allowing analyses to access all Gene Ontology annotations--updated monthly from the Gene

109 alyses were performed in silico according to Gene Ontology annotations.

110 We used a gene ontology approach to analyze variants and identifie

111 opsis RNA-seq dataset resulting in disparate gene ontologies arising from gene set enrichment analyse

112 The most significant gene ontology association of Runx1-Pu.1 co-bound genes w

113 ons of individual genomes to pangenomes with gene ontology based navigation of gene groups.

114 Furthermore, the result of Gene Ontology-based term classification (GO), EuKaryotic

115 Our Gene Ontology-based term classification and KEGG-based p

116 Among the enriched Gene Ontology Biological Process (GO BP) terms, actin cy

117 Gene ontology biological process term analysis revealed

118 With the generated signaling network and gene ontology biological process term grouping, we ident

119 Four gene ontology biological processes were enriched among g

120 revealed highly significant enrichments for Gene Ontology biological processes, pathway maps, and pr

121 henotype ontology and transfer learning with gene ontology can improve the predictions.

122 Two gene ontology categories accounted for half of the loci

123 element contents of FP and EFP, and enriched Gene Ontology categories also differed.

124 ional analyses identifies epigenetics marks, gene ontology categories and disease GWAS loci affected

125 Over-represented Gene Ontology categories are reported here.

126 to genomic features such as genes and their gene ontology categories could increase the accuracy of

127 ross-validation and meaningful enrichment of gene ontology categories within genes classified as high

128 genetics on microbial species, pathways and gene ontology categories, on the basis of metagenomic se

129 enes were significantly enriched for several Gene Ontology categories.

130 ng neuronal functions including postsynaptic gene ontology categories.

131 ory electron chain and ATP synthesis related gene ontology-categories were upregulated in GoF salmon,

132 r-representation of L1 insertions within the gene ontologies 'cell projection' and 'postsynaptic memb

133 ach, and applied this in a reanalysis of the gene ontology classes of targets of miRNA lists from 44

134 Gene function enrichment identified 158 gene ontology classes that were overrepresented in flora

135 gnificant differentially expressed proteins, gene ontology classification, and pathway representation

136 le in their degradation by XRN4 and VCS, and Gene Ontology clustering revealed novel actors of seed d

137 Gene Ontology clustering showed that the functions of po

138 iew of the gene name, aliases, phenotype and Gene Ontology curation, whereas other tabs display more

139 issProt database and annotated by collecting gene ontology data from databases and existing literatur

140 Rs were searched against Non-redundant (Nr), Gene Ontology database (GO), eukaryotic orthologous grou

141 tes and used the GRAIL algorithm to mine the Gene Ontology database for evidence of functional connec

142 e groups independently validated by DAVID, a gene ontology database, with FDR < 0.05.

143 tology annotations--updated monthly from the Gene Ontology database--in addition to the annotations t

144 ical significance of data with regard to the Gene Ontology database.

145 d proteome annotation databases, such as the Gene Ontology database.

146 multiple ontology categories (like pathways, gene ontology, disease categories) and therefore expedit

147 combination of spatial molecular network and gene ontology enrichment analyses, it is shown that gene

148 Gene ontology enrichment analysis and protein-protein in

149 Gene ontology enrichment analysis from gene-expression d

150 th a high similarity to Arabidopsis APETALA1 Gene Ontology enrichment analysis of differentially expr

151 able of their target genes, accompanied by a Gene Ontology enrichment analysis of the biological proc

152 Following Gene Ontology enrichment analysis of the up- or down- re

153 time course expression profiles, clustering, gene ontology enrichment analysis, differential expressi

154 erenhancer signal profile, associated genes, gene ontology enrichment analysis, motifs of transcripti

155 ound to be biologically significant based on Gene Ontology enrichment analysis, pathway analysis, and

156 Gene ontology enrichment and pathway analysis showed pre

157 Gene ontology enrichment revealed a repression of lignin

158 Differential expression analysis and Gene ontology enrichment revealed that the number of tra

159 lated genes based on statistical validation, gene ontology enrichment, differential expression betwee

160 idation tests (Edge Set Enrichment Analysis, Gene Ontology Enrichment, Disease-Gene Subnetwork Compac

161 By assessing gene ontology enrichment, we determined the potential mR

162 ee categories of protein functions including gene ontology, enzyme commission and ligand-binding site

163 from other sources such as InterProScan, the Gene Ontology, ENZYME, UniPathway, and others.

164 e ontologies, with the interferon-associated gene ontology exhibiting the highest percentage of upreg

165 for scoring and summarising the capacity of gene ontology features to simultaneously classify sample

166 twork via a pairwise comparison of all yeast genes' Ontology Fingerprints--a set of Gene Ontology ter

167 pments in OMA: (i) a new web interface; (ii) Gene Ontology function predictions as part of the OMA pi

168 We identify numerous modules enriched for Gene Ontology functions, and show that modules conserved

169 ion data with external sources of orthology, gene ontology, gene interaction and pathway information.

170 ific targeting for >5000 mRNAs we determined gene ontologies (GO).

171 chemokine ligand 4 (CCL4), while exploratory gene ontology (GO) analyses revealed lower expression of

172 Gene Ontology (GO) analysis and Kyoto Encyclopedia of Ge

173 We performed Gene Ontology (GO) analysis for genes available in final

174 Microarray and gene ontology (GO) analysis identified inhibin beta-B (I

175 Gene ontology (GO) analysis of the co-expression modules

176 Through Gene Ontology (GO) analysis, the target genes were mappe

177 Based on Gene Ontology (GO) analysis, two approaches were used to

178 The dataset was assembled and annotated via Gene Ontology (GO) analysis.

179 Gene ontology (GO) and pathway analyses revealed the maj

180 Results from Gene Ontology (GO) and pathway enrichment analysis revea

181 mplemented in the short term both to improve Gene Ontology (GO) annotation coverage based on annotati

182 hrough a cross-validation analysis using the Gene Ontology (GO) annotation of a sub-set of UniProtKB/

183 , a new browser for the PDB archive based on Gene Ontology (GO) annotation, updates to the analysis o

184 ted function prediction method that predicts Gene Ontology (GO) annotations for a protein sequence us

185 ent the FunFHMMer web server, which provides Gene Ontology (GO) annotations for query protein sequenc

186 analysed by semantic comparison of enriched gene ontology (GO) annotations of the target gene sets f

187 esent the wiring around proteins in PINs and gene ontology (GO) annotations to describe their functio

188 ation (GOA) resource provides evidence-based Gene Ontology (GO) annotations to proteins in the UniPro

189 icals/drugs, genes/proteins, diseases, taxa, Gene Ontology (GO) annotations, pathways, and gene inter

190 ntegrating the hierarchical structure of the Gene Ontology (GO) data dramatically improves prediction

191 ng of the predicted modules by utilizing the Gene Ontology (GO) database as gold standard for the def

192 , mPLR-Loc exploits the information from the Gene Ontology (GO) database by using its accession numbe

193 he existing annotation catalogs, such as the Gene Ontology (GO) database.

194 genes were characterized using the MetaCore Gene Ontology (GO) enrichment analysis algorithm.

195 Gene ontology (GO) enrichment analysis of nod+ specific

196 howed same effect directions in stage-2, the gene ontology (GO) enrichment analysis showed several si

197 We also performed a gene ontology (GO) enrichment analysis.

198 Gene Ontology (GO) has been used widely to study functio

199 dug out and classified functionally using a gene ontology (GO) hierarchy, followed by KEGG pathway e

200 Additional evaluations using gene ontology (GO) indicate that significant enrichment

201 Gene ontology (GO) is a widely used resource to describe

202 Although Gene Ontology (GO) is available for Caenorhabditis elega

203 well-defined structure and manual curation, Gene Ontology (GO) is the most frequently used vocabular

204 While the manually curated Gene Ontology (GO) is widely used, inferring a GO direct

205 ious publicly available sources and uses the Gene Ontology (GO) project term relationships to produce

206 The Gene Ontology (GO) provides biologists with a controlled

207 s of treated and untreated C. albicans using Gene Ontology (GO) revealed a large cluster of down regu

208 Gene ontology (GO) term analysis showed that most of the

209 performing predictors proposed recently use gene ontology (GO) terms to construct feature vectors fo

210 Likewise, 477 and 16 Gene Ontology (GO) terms were significantly enriched in

211 Gene Ontology (GO) terms with abundance of (AG)3-Di-SSRs

212 ch leads to the creation of more descriptive Gene Ontology (GO) terms, as well as an increase in both

213 detecting genomic features, here defined by gene ontology (GO) terms, enriched for causal variants a

214 e information on genes, biological pathways, Gene Ontology (GO) terms, gene-gene interaction networks

215 Gene ontology (GO) terms, GO:0010200 (response to chitin

216 ns with non-enzymatic functions annotated by Gene Ontology (GO) terms.

217 be expression and we assessed enrichment for gene ontology (GO) terms.

218 ir function, properties and complex-specific Gene Ontology (GO) terms.

219 Recently, a shift was made from using Gene Ontology (GO) to evaluate molecular network data to

220 In this work, a novel weighted Gene Ontology (GO) transfer model is proposed to generat

221 The Gene Ontology (GO), a valuable and widely-used resource

222 d functional annotation databases, i.e., the Gene Ontology (GO), are far from being complete.

223 acteristic features of MPs, which range from gene ontology (GO), protein-protein interactions, gene e

224 e here focus on relating two ontologies: the Gene Ontology (GO), which encodes canonical gene functio

225 edical and biological ontologies such as the Gene Ontology (GO).

226 enes using standardized nomenclature such as Gene Ontology (GO).

227 LP) to incorporate functional annotations in Gene Ontology (GO).

228 Differential gene expression (fold-change), gene ontology (GO; biological process) and pathway analy

229 dure whenever the logical assumptions of the Gene Ontology graph structure are appropriate for the st

230 and next generation sequencing studies using Gene Ontology graphs, which we call the Short Focus Leve

231 demonstrated that significantly lowest-level Gene Ontology groups in changes of gene expression in bl

232 Gene Ontology identification of human orthologs to the s

233 In patients with MDS/AML, gene ontology (ie, secondary-type AML carrying mutations

234 on involved significant enrichment of select gene ontologies, in particular, enrichment of genes invo

235 Enrichment categories for gene ontology included ion transport, synaptic transmiss

236 The Network-extracted Ontology (NeXO) is a gene ontology inferred directly from large-scale molecul

237 We used the Gene Ontology, Ingenuity, KEGG, Panther, Reactome, and B

238 cally defined metabolite sets developed from Gene Ontology, KEGG and Medical Subject Headings, using

239 We also carried out detailed gene ontology, KEGG, disease association, pathway common

240 gh-density microarrays and pathway analyses (Gene Ontology, Kyoto Encyclopedia of Genes and Genomes,

241 annotated with MIPS Functional Catalogue and Gene Ontology labels.

242 urately classify HRGPs leads to inconsistent gene ontologies limiting the identification of HRGP clas

243 and colon organoids, along with RNA-Seq and gene ontology methods, to characterize the effects of IL

244 A combined analysis of Gene Ontology, microRNA targets and transcription factor

245 cular function collection, defined by shared Gene Ontology molecular function.

246 Gene ontology of predicted target genes for COMs showed

247 Gene ontology of these 491 proteins singled out the acti

248 done in the context of pathway information, gene ontology or any custom annotation of the data.

249 ta that has a hierarchical component such as gene ontology or geographic location data.

250 on and 5hmC profiles that mapped to specific gene ontology pathways.

251 ly updated using the models generated by the Gene Ontology Phylogenetic Annotation Project.

252 ave more than doubled due to progress of the Gene Ontology Phylogenetic Annotation Project.

253 The Gene Ontology project integrates data about the function

254 nrichment analysis revealed common pathways, gene ontology, protein domains, and cell type-specific e

255 including protein-protein interaction (PPI), gene ontology, protein tertiary structures, orthologous

256 We present GOssTo, the Gene Ontology semantic similarity Tool, a user-friendly

257 analysis (GSA) using canonical pathways and gene ontology sets, 3) weighted gene co-expression netwo

258 Analysis of gene ontology showed that bidirectional genes tend to ha

259 protein interactome was distinct, and with a gene ontology signal for mitochondrial regulation which

260 We applied gene ontology software to find enriched biological meani

261 The problem of GO (gene ontology) sparsity is tackled by introducing the ho

262 ion of COI1 and enrichment of genes with the Gene Ontology term 'cullin-RING ubiquitin ligase complex

263 Gene ontology term enrichment analysis of 416 genes from

264 Gene ontology term enrichment analysis was used to explo

265 annotation for circulating miRNAs using the gene ontology term extracellular space as part of blood

266 ct to protein complex prediction, high level Gene Ontology term prediction and especially sparse modu

267 that the genes in a gene set share the same Gene Ontology term so that they are involved in the same

268 y all of them are enriched with at least one gene ontology term.

269 gions (ConSurf), homology-based inference of Gene Ontology terms (metastudent), comprehensive subcell

270 Using Gene Ontology terms and genome databases, 1805 genes wer

271 tic signatures implicates a wide spectrum of Gene Ontology terms and KEGG pathways with condition-spe

272 chanisms (defined by the gene sets), such as Gene Ontology terms and molecular pathways.

273 The predicted target genes were described in Gene Ontology terms and were found to be involved in a b

274 Gene ontology terms associated with development, neuron

275 functional analysis revealed enrichment for Gene Ontology terms associated with neural function and

276 mies and 33 loci with microbial pathways and gene ontology terms at P < 5 x 10(-8).

277 6-FLAG-YFP-NL2 mice showed enrichment in the Gene Ontology terms cell-cell signaling and synaptic tra

278 Gene ontology terms for "extracellular space" and "defen

279 of significant genomic regions enriched with Gene Ontology terms for DNA repair.

280 yeast genes' Ontology Fingerprints--a set of Gene Ontology terms overrepresented in the PubMed abstra

281 While approximately 60% of Gene Ontology terms previously associated with guard cel

282 tion has identified a large number of unique gene ontology terms related to metabolic activities, a r

283 The presence of a number of overrepresented Gene Ontology terms related to plant defense in the set

284 These genes are highly enriched for Gene Ontology terms related to the extracellular matrix,

285 nt FFPred 3, which is intended for assigning Gene Ontology terms to human protein chains, when homolo

286 l is described and the process of mapping of Gene Ontology terms to InterPro is outlined.

287 annotations to derive a ranking of genes and gene ontology terms using a supervised learning approach

288 Among the Gene Ontology terms which were identified as grazing res

289 tegorized into 27 functional groups based on Gene Ontology terms, including 14 groups in biological p

290 ns of OGs have been expanded to also provide Gene Ontology terms, KEGG pathways and SMART/Pfam domain

291 e-defined gene sets, such as known pathways, gene ontology terms, or other experimentally derived gen

292 ally, 43 putative lincRNAs were annotated by Gene Ontology terms.

293 ere annotated and 50% could be assigned with Gene Ontology terms.

294 tion consistency was examined by analysis of Gene Ontology terms.

295 re first collapsed at the gene level then by Gene Ontology terms.

296 As predicted, metabolic pathways and gene ontologies that are putatively dosage-sensitive bas

297 analysis for protein sets annotated with the Gene Ontology, there are only a few that can be used for

298 Using gene ontology, transgenic lines, and in situ hybridizati

299 eptide interactions follow human-constructed gene ontologies, which suggest that our understanding of

300 the upregulation of multiple proinflammatory gene ontologies, with the interferon-associated gene ont