ライフサイエンスコーパス: ontology

1 with Kyoto Encyclopedia of Genes and Genomes ontology.

2 terms and the hierarchical structure of the ontology.

3 r bio-entity recognizer based on the Protein Ontology.

4 th the use of terms from the Human Phenotype Ontology.

5 the identified clusters using an appropriate ontology.

6 ss labels extracted from the Mouse Phenotype Ontology.

7 aradigm for cell type definition in the Cell Ontology.

8 tissue phenotypes expressed using controlled ontologies.

9 m advanced SPARQL queries of one or multiple ontologies.

10 at leverage linkage of CL, CLO and other bio-ontologies.

11 depth of proteins within hierarchies of gene ontologies.

12 n the biological process and human phenotype ontologies.

13 nd complementary to the genetic and chemical ontologies.

14 regards to introns, genes, and affected gene ontologies.

15 rents to specific classes in domain-specific ontologies.

16 ius set of performant functions for querying ontologies.

17 extracted from the transcriptomes using Gene Ontology, adult-brain gene lists generated by Translatin

18 mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the

19 Gene ontology analyses revealed activation of metabolic proce

20 r driving the pathway associations, and gene ontology analysis demonstrated enrichment for calcium tr

21 Furthermore, gene ontology analysis demonstrated that DMRs associated with

22 Gene ontology analysis demonstrates that a large portion of E

23 Gene Ontology analysis demonstrates that HA-CS NPs were the l

24 Gene ontology analysis identified a novel role for Matrin 3 i

25 al overlap revealed through comparative gene ontology analysis in both species.

26 Gene ontology analysis indicated that their main molecular fu

27 Gene ontology analysis indicates reduced metabolic processing

28 Through gene ontology analysis of Akt-regulated PRB target genes, ang

29 In human liver cancers, ontology analysis of gene set enrichment analysis (GSEA)

30 Gene ontology analysis of genes with DMCs at TSSs revealed an

31 Gene ontology analysis of highly represented genes from the p

32 function of mitochondria, according to gene ontology analysis of proteins that are down-regulated by

33 Gene ontology analysis of SR1-regulated genes confirmed previ

34 Gene Ontology analysis of the binding partners revealed a sig

35 Gene ontology analysis of the secretory proteins revealed an

36 Pathway and gene ontology analysis revealed differential expression of fu

37 Gene ontology analysis revealed enrichment in processes assoc

38 Gene ontology analysis revealed enrichment of cell migration

39 Gene ontology analysis revealed enrichment of signaling molec

40 Gene ontology analysis revealed enrichment of smoking-related

41 Gene Ontology analysis revealed that nuclear lumen, nuclear p

42 Gene ontology analysis revealed that proteins in numerous cel

43 Gene Ontology analysis revealed that S-(+)-fipronil caused mo

44 A gene ontology analysis reveals that stress response processes

45 Gene ontology analysis showed that genes affected by TRAF3IP2

46 and stress fiber formation by TGF-beta, gene ontology analysis showed that genes encoding extracellul

47 Gene Ontology analysis using loci bearing unique GDM- and pre

48 oduction, principal component analysis, gene ontology analysis, and dynamic network analysis.

49 analysis, principal component analysis, gene ontology analysis, and network analysis) or multiple typ

50 evant genes which are well justified by gene ontology analysis.

51 standardized formal patterns for structuring ontologies and annotations and for linking ontologies to

52 data-driven cell classification to structure ontologies and integrate them with data-driven cell quer

53 ied 59% of the cell type classes in the Cell Ontology and 13% of the cell line classes in the Cell Li

54 and mutations, development of a unique Model Ontology and accompanying AMR detection models to power

55 ries, each of which is classified within the ontology and assigned multiple annotations including (wh

56 Gene ontology and biological pathways analyses revealed signi

57 y to predict biological functions using Gene Ontology and gene-disease associations using Human Pheno

58 This hypothesis was corroborated by gene ontology and global interactome analyses, which highligh

59 Gene Ontology and KEGG pathway analyses shed light on the pot

60 Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pat

61 lved in the regulation of miR-124-3p by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pat

62 chical structure among the phenotypes in the ontology and leverage the sparse known associations with

63 the primary components of the Bio-TDS is the ontology and natural language processing workflow for an

64 Gene ontology and network analyses showed enrichment of genes

65 ovineMine will be especially useful for gene ontology and pathway analyses in conjunction with GWAS a

66 Ontology and pathway analyses were done on lists of gene

67 o the function of the DEGs, we examined gene ontology and pathway and phenotype enrichment and found

68 o the function of the DEGs, we examined gene ontology and phenotype enrichment and found significant

69 erarchically organized based on Experimental Ontology and Plant Ontology so that users can browse, se

70 Gene Ontology and PubMatrix analyses of differentially expres

71 erence-transcriptome-guided approach on gene ontology and tox-pathways, we confirmed the novel approa

72 demonstrate how phenotype paths in phenotype ontology and transfer learning with gene ontology can im

73 Classification of Diseases, Human Phenotype Ontology and Unified Medical Language System) and also c

74 d functional gene annotation, and anatomical ontologies, and a new collection of full ORF cDNAs.

75 ntially expressed transcripts, enriched gene ontology, and altered functions and canonical pathways p

76 Gene ontology annotation and network analysis showed that MAL

77 e in the circulation, we also created a gene ontology annotation for circulating miRNAs using the gen

78 notype), and molecular data types (e.g. Gene Ontology Annotation, protein interactions), as well as l

79 tension packages and publicly available gene ontology annotations facilitates straightforward integra

80 information of individual samples with gene ontology annotations to derive a ranking of genes and ge

81 new tools for browsing genomic features and ontology annotations.

82 s were performed in silico according to Gene Ontology annotations.

83 We used a gene ontology approach to analyze variants and identified ove

84 Nowadays, ontologies are one of the crucial enabling technologies

85 Ontologies are widely used constructs for encoding and a

86 It is built upon the Antibiotic Resistance Ontology (ARO), a custom built, interconnected and hiera

87 linking concepts across multiple biomedical ontologies as well as non-structural biomedical knowledg

88 f individual genomes to pangenomes with gene ontology based navigation of gene groups.

89 recently suggested time-symmetric Heisenberg ontology based on nonlocal deterministic operators.

90 developed the Molecular Biology of the Cell Ontology based on standard cell biology and biochemistry

91 reference genomes that are integrated using ontology-based annotation and comparative analyses, and

92 Validated against literature- and disease ontology-based approaches, analysis of 39 disease/trait-

93 ProbOnto, is an ontology-based knowledge base of probability distributio

94 However, biomedical ontology-based named entity recognition continues to be

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

96 Our Gene Ontology-based term classification and KEGG-based pathwa

97 Among the enriched Gene Ontology Biological Process (GO BP) terms, actin cytoske

98 Gene ontology biological process term analysis revealed that

99 ith the generated signaling network and gene ontology biological process term grouping, we identify p

100 Four gene ontology biological processes were enriched among genes

101 of data-driven cell-classifications and how ontologies can be extended by integrating the outputs of

102 Our results show that structured clinical ontologies can be used to determine the degree of overla

103 mples illustrating how formalization of cell ontologies can enhance querying of data-driven cell-clas

104 ype ontology and transfer learning with gene ontology can improve the predictions.

105 We conclude that this ontology can, from omics data sets, enable the developme

106 fy clusters of genes that belong to multiple ontology categories (like pathways, gene ontology, disea

107 tics on microbial species, pathways and gene ontology categories, on the basis of metagenomic sequenc

108 uronal functions including postsynaptic gene ontology categories.

109 resentation of L1 insertions within the gene ontologies 'cell projection' and 'postsynaptic membrane'

110 The Cell Ontology (CL) and Cell Line Ontology (CLO) have long bee

111 gating across proteins and diseases based on ontology classes, and displays a scatter plot with two p

112 The Cell Ontology (CL) and Cell Line Ontology (CLO) have long been established as reference o

113 The Cell Line Ontology (CLO) is an OBO community-based ontology that c

114 The community-based Cell Line Ontology (CLO) is selected as the default ontology for L

115 cell line representation from the Cell Line Ontology (CLO).

116 their degradation by XRN4 and VCS, and Gene Ontology clustering revealed novel actors of seed dorman

117 tegrates and extends ontologies from the bio-ontology community to drive a number of practical applic

118 ChEBI is a database and ontology containing information about chemical entities

119 Currently, our ontology contains 5,384 genes, 753 SCPs and 19,180 exper

120 Ontobee is a linked ontology data server that stores ontology information us

121 ups independently validated by DAVID, a gene ontology database, with FDR < 0.05.

122 significance of data with regard to the Gene Ontology database.

123 ges to be addressed when developing a common ontology design pattern for representing cell lines in b

124 ardisation of the cell nomenclature based on ontology development to support FAIR principles of the c

125 ple ontology categories (like pathways, gene ontology, disease categories) and therefore expedite sci

126 Phenotype Ontology (HPO) terms, 435 Disease Ontology (DO) terms and 228 Disease Ontology Lite (DOLit

127 y supporting the interoperability in the bio-ontology domain.

128 ntal Factor Ontology (EFO) is an application ontology driven by experimental variables including cell

129 GS) transcriptomic analysis results using an ontology-driven database.

130 each card is mapped to popular hierarchical ontologies (e.g. International Classification of Disease

131 The Experimental Factor Ontology (EFO) is an application ontology driven by expe

132 n ontologies such as the Experimental Factor Ontology (EFO), and the Ontology for Biomedical Investig

133 s has differential connectivity and distinct ontologies (eg, proapoptosis enriched in network of good

134 Since a common drawback to ontology enrichment analyses is its verbosity, we develo

135 nation of spatial molecular network and gene ontology enrichment analyses, it is shown that genes inv

136 Gene ontology enrichment analysis and protein-protein interac

137 Gene ontology enrichment analysis from gene-expression data r

138 high similarity to Arabidopsis APETALA1 Gene Ontology enrichment analysis of differentially expressed

139 course expression profiles, clustering, gene ontology enrichment analysis, differential expression an

140 ancer signal profile, associated genes, gene ontology enrichment analysis, motifs of transcription fa

141 Differential expression analysis and Gene ontology enrichment revealed that the number of transcri

142 on tests (Edge Set Enrichment Analysis, Gene Ontology Enrichment, Disease-Gene Subnetwork Compactness

143 By assessing gene ontology enrichment, we determined the potential mRNA ta

144 tegories of protein functions including gene ontology, enzyme commission and ligand-binding sites fro

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

146 ologies, with the interferon-associated gene ontology exhibiting the highest percentage of upregulate

147 rces exist, no suitably detailed and complex ontology exists nor any database allowing programmatic a

148 use of the zebrafish experimental conditions ontology, 'Fish' records in the ZFIN database, support f

149 ver implicit relatedness between concepts in ontologies for which potentially valuable relationships

150 Experimental Factor Ontology (EFO), and the Ontology for Biomedical Investigation (OBI).

151 It tests a slimmed-down C. elegans tissue ontology for enrichment of specific terms and provides u

152 ne Ontology (CLO) is selected as the default ontology for LINCS cell line representation and integrat

153 The Protein Ontology formally defines and describes taxon-specific a

154 EFO integrates and extends ontologies from the bio-ontology community to drive a nu

155 ata with external sources of orthology, gene ontology, gene interaction and pathway information.

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

157 kine ligand 4 (CCL4), while exploratory gene ontology (GO) analyses revealed lower expression of immu

158 Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes a

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

160 Microarray and gene ontology (GO) analysis identified inhibin beta-B (Inhbb)

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

162 Through Gene Ontology (GO) analysis, the target genes were mapped in

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

164 Gene ontology (GO) and pathway analyses revealed the major pa

165 h a cross-validation analysis using the Gene Ontology (GO) annotation of a sub-set of UniProtKB/Swiss

166 /drugs, genes/proteins, diseases, taxa, Gene Ontology (GO) annotations, pathways, and gene interactio

167 ating the hierarchical structure of the Gene Ontology (GO) data dramatically improves prediction accu

168 s were characterized using the MetaCore Gene Ontology (GO) enrichment analysis algorithm.

169 Gene ontology (GO) enrichment analysis of nod+ specific DEGs

170 same effect directions in stage-2, the gene ontology (GO) enrichment analysis showed several signifi

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

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

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

174 Although Gene Ontology (GO) is available for Caenorhabditis elegans, i

175 -defined structure and manual curation, Gene Ontology (GO) is the most frequently used vocabulary for

176 treated and untreated C. albicans using Gene Ontology (GO) revealed a large cluster of down regulated

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

178 orming predictors proposed recently use gene ontology (GO) terms to construct feature vectors for cla

179 Likewise, 477 and 16 Gene Ontology (GO) terms were significantly enriched in CHB a

180 cting genomic features, here defined by gene ontology (GO) terms, enriched for causal variants affect

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

182 th non-enzymatic functions annotated by Gene Ontology (GO) terms.

183 pression and we assessed enrichment for gene ontology (GO) terms.

184 istic features of MPs, which range from gene ontology (GO), protein-protein interactions, gene expres

185 o incorporate functional annotations in Gene Ontology (GO).

186 erential gene expression (fold-change), gene ontology (GO; biological process) and pathway analyses w

187 rence for cell type representation, the Cell Ontology has been developed to provide a standard nomenc

188 t the International Conference on Biomedical Ontology has brought together experimental biologists an

189 ay to analyze these datasets is to associate ontologies (hierarchical, descriptive vocabularies with

190 The Human Phenotype Ontology (HPO) allows composite phenotypes to be represe

191 In this context the Human Phenotype Ontology (HPO) provides a standard categorization of the

192 pped 1610 GWAS traits to 501 Human Phenotype Ontology (HPO) terms, 435 Disease Ontology (DO) terms an

193 itution method to enrich the Human Phenotype Ontology (HPO) with new synonyms.

194 enotype-gene associations in Human Phenotype Ontology (HPO).

195 The three components of the Human Phenotype Ontology (HPO; www.human-phenotype-ontology.org) project

196 e the International Conference on Biomedical Ontology (ICBO).

197 Gene Ontology identification of human orthologs to the strong

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

199 CLO) have long been established as reference ontologies in the OBO framework.

200 erver for publishing and browsing biomedical ontologies in the Open Biological Ontology (OBO) Foundry

201 Our mapping pipeline illustrates the use of ontology in aiding biological data standardization and i

202 This is consistent with a holistic ontology in the German Romantic tradition.

203 3% of the cell line classes in the Cell Line Ontology in the literature.

204 ourage wider adoption of the Human Phenotype Ontology in the study of rare genetic diseases.

205 Ontobee currently hosts more than 180 ontologies (including 131 OBO Foundry Library ontologies

206 is a linked ontology data server that stores ontology information using RDF triple store technology a

207 The Cell Component Ontology is freely available.

208 ction of phenotypes organized in a phenotype ontology, it is crucial to effectively model the hierarc

209 defined metabolite sets developed from Gene Ontology, KEGG and Medical Subject Headings, using both

210 We also carried out detailed gene ontology, KEGG, disease association, pathway commons, Wi

211 nsity microarrays and pathway analyses (Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, Gene

212 olves mapping samples to terms in biomedical ontologies, labeling each sample with a sample-type cate

213 ly classify HRGPs leads to inconsistent gene ontologies limiting the identification of HRGP classes i

214 rmation are generated and displayed for each ontology listed in Ontobee.

215 Disease Ontology (DO) terms and 228 Disease Ontology Lite (DOLite) terms.

216 complexity of genomic information and public ontologies, making sense of these datasets demands integ

217 colon organoids, along with RNA-Seq and gene ontology methods, to characterize the effects of IL28 on

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

219 biomedical ontologies in the Open Biological Ontology (OBO) Foundry library.

220 ious datasets, producing a second-generation ontology of 220 functions.

221 Based on the Ontology of Adverse Events (OAE) hierarchical classifica

222 Extending the Ontology of AEs (OAE) and NDF-RT, OCVDAE includes 194 CV

223 used in China, and developed and analyzed an Ontology of Cardiovascular Drug AEs (OCVDAE).

224 Using this process, we assemble an ontology of functions comprising autophagy, a central re

225 Gene ontology of predicted target genes for COMs showed that

226 I (Chemical Entities of Biological Interest) ontology of small molecules.

227 Gene ontology of these 491 proteins singled out the actin cyt

228 The aim of this work was to characterise the ontology of these four muscle-specific miRNAs in the blo

229 e-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species.

230 the SCPs with genes enables extension of the ontology on demand and the adaption of the ontology to t

231 lysis tool, BiNChE, and a query tool for the ontology, OntoQuery.

232 tion of relevant semantic standards, such as ontologies or hierarchical vocabularies, has lagged.

233 at has a hierarchical component such as gene ontology or geographic location data.

234 Phenotype Ontology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, dise

235 d 5hmC profiles that mapped to specific gene ontology pathways.

236 dated using the models generated by the Gene Ontology Phylogenetic Annotation Project.

237 ore than doubled due to progress of the Gene Ontology Phylogenetic Annotation Project.

238 (Application Programming Interface) enables ontology-powered search for and retrieval of CRAM, bigwi

239 ment analysis revealed common pathways, gene ontology, protein domains, and cell type-specific expres

240 The genetic and environmental ontology reconstructed from the omics data is substantia

241 tis) (1) up-regulated genes were enriched in ontologies related to B-cell homing and activation; (2)

242 es rapid identification and visualisation of ontology-related gene panels that robustly classify grou

243 t knowledge) may exist, we developed a Multi-Ontology Relatedness Model (MORM) within the knowledge n

244 echnologies has led us to the requirement of ontology representation of cell types and cell lines.

245 ) proposes a bottom-up approach to cognitive ontology revision: Neuroscientists should revise their t

246 linkage disequilibrium and eQTL data, and an ontology search for phenotypes, traits and disease.

247 in interactome was distinct, and with a gene ontology signal for mitochondrial regulation which was c

248 ery simple filtering algorithm to reduce the ontology size by an order of magnitude.

249 zed based on Experimental Ontology and Plant Ontology so that users can browse, search, and retrieve

250 e definition of top-performing algorithms is ontology specific, that different performance metrics ca

251 is is an established approach in data-driven ontologies such as the Experimental Factor Ontology (EFO

252 phical user interface (www.ebi.ac.uk/gwas/), ontology supported search functionality and an improved

253 f COI1 and enrichment of genes with the Gene Ontology term 'cullin-RING ubiquitin ligase complex' in

254 Gene ontology term enrichment analysis of 416 genes from the

255 Gene ontology term enrichment analysis was used to explore sh

256 tation for circulating miRNAs using the gene ontology term extracellular space as part of blood plasm

257 ogy, Ontobee is able to dereference a single ontology term URI, and then output RDF/eXtensible Markup

258 For each ontology term, we also predicted the putative causal gen

259 zing the details and hierarchy of a specific ontology term.

260 of them are enriched with at least one gene ontology term.

261 Using Gene Ontology terms and genome databases, 1805 genes were ide

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

263 redicted target genes were described in Gene Ontology terms and were found to be involved in a broad

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

265 Annotation with ontology terms can play an important role in making data

266 Structured annotation with ontology terms provides a potential solution to these pr

267 has identified a large number of unique gene ontology terms related to metabolic activities, a region

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

269 Pred 3, which is intended for assigning Gene Ontology terms to human protein chains, when homology wi

270 ations to derive a ranking of genes and gene ontology terms using a supervised learning approach.

271 The SNP Annotator adds traits, ontology terms, effects and interactions to markers in a

272 ized into 27 functional groups based on Gene Ontology terms, including 14 groups in biological proces

273 OGs have been expanded to also provide Gene Ontology terms, KEGG pathways and SMART/Pfam domains for

274 ined gene sets, such as known pathways, gene ontology terms, or other experimentally derived gene set

275 supports query, visualization and linkage of ontology terms.

276 rst collapsed at the gene level then by Gene Ontology terms.

277 features were recorded using Human Phenotype Ontology terms.

278 43 putative lincRNAs were annotated by Gene Ontology terms.

279 as well as sample attributes annotated with ontology terms.

280 As predicted, metabolic pathways and gene ontologies that are putatively dosage-sensitive based on

281 percent in the semantic classes of the eight ontologies that have been annotated in earlier versions

282 This approach produces a simple ontology that captures TNBC heterogeneity and informs ho

283 ine Ontology (CLO) is an OBO community-based ontology that contains information of immortalized cell

284 e the annotation of assays and targets using ontologies, the inclusion of targets and indications for

285 everage the structure of the classifications/ontologies; the tools also allow users to upload genetic

286 e been identified in many genes with varying ontologies, therein indicating the diverse molecules and

287 The ontologyIndex package enables arbitrary ontologies to be read into R, supports representation of

288 H utilizes structure similarity and chemical ontologies to map all known metabolites and name metabol

289 e development and applications of biomedical ontologies to represent and analyze experimental cell da

290 g ontologies and annotations and for linking ontologies to the outputs of data-driven classification.

291 We then describe an ontology to both annotate these models and capture the i

292 ains of life and extended our Cell Component Ontology to enable representation of the inferred archit

293 pdated CLO will be examined as the candidate ontology to import and replace eligible EFO cell line cl

294 e ontology on demand and the adaption of the ontology to the continuously growing cell biological kno

295 (RN) is defined and computed across multiple ontologies using a formal inference mechanism of set-the

296 Text labels of cell lines from both ontologies were verified by biological information axiom

297 Ontologies, which logically represent entities and relat

298 ther development, including incorporation of ontologies, will be necessary to improve the performance

299 ntologies (including 131 OBO Foundry Library ontologies) with over four million terms.

300 pregulation of multiple proinflammatory gene ontologies, with the interferon-associated gene ontology