ライフサイエンスコーパス: ontology

1 of genes enriched for the inflammation gene ontology.

2 velopment of a behaviour change intervention ontology.

3 tainty reasoning is important in researching ontology.

4 could be considered for addition to the Cell Ontology.

5 undry principles as an extension of the Cell Ontology.

6 hierarchical structure derived from the Gene Ontology.

7 ss-database study discovery using a range of ontologies.

8 tencies in the use of metadata standards and ontologies.

9 ies and (3) assess the quality of identified ontologies.

10 based on global gene expression patterns and ontologies.

11 from existing taxonomies, terminologies and ontologies.

12 nd investigation-related terms from existing ontologies.

13 cations but is missing from existing medical ontologies.

14 ctronic medical record (EMR) texts to enrich ontologies.

15 ipulated conditions, using formal anatomical ontologies.

16 We have developed the Cancer Cell Ontology according to OBO Foundry principles as an exten

17 These similarities suggest broader genetic ontology accounts for aspects of phenotypic heterogeneit

18 cal Subject Headings and the Human Phenotype Ontology allows the precise identification of genetic va

19 In lung and pancreatic cells, gene ontology analyses of DM promoters show an enrichment for

20 Gene ontology analyses of genes coexpressed between extraembr

21 Pathway and gene ontology analyses of these differentially expressed gene

22 Gene ontology analyses of transcripts displaying APA in respo

23 MapMan and Gene Ontology analyses revealed global transcriptional change

24 d to herbivory followed by combined KEGG and ontology analyses.

25 scale modeling, ribosome profiling, and gene ontology analyses.

26 Gene ontology analysis highlighted several new targets and pa

27 Functional ontology analysis of the adductome indicated that protei

28 Gene ontology analysis of the budesonide-modulated transcript

29 Gene ontology analysis of the gene panel identified multiple

30 Gene Ontology analysis of this cluster revealed enrichment fo

31 Gene ontology analysis of three independent clinical GC cohor

32 Gene ontology analysis of TOPBP1- and ETAA1-dependent phospho

33 Gene ontology analysis revealed decreased synaptic signaling

34 Gene ontology analysis revealed multicellular organism develo

35 Gene ontology analysis revealed that a number of genetic fact

36 Gene ontology analysis showed that expression of PIEZO1 was c

37 Gene ontology analysis suggested that Nrf2 KO-changed protein

38 gene coexpression network analysis and gene ontology analysis to identify biological processes assoc

39 Gene set enrichment analysis and gene ontology analysis was used to examine transcriptomic cha

40 ins and 10 autoantigens identified from gene ontology analysis were combined with 48 proteins identif

41 Gene ontology analysis, focusing on genes with low, intermedi

42 ) describe the methods used to develop these ontologies and (3) assess the quality of identified onto

43 propose an implementation based on phenotype ontologies and Bayesian probability updates.

44 Public ontologies and controlled vocabularies are used for anno

45 gions Search tool, integration of additional ontologies and development of many new template queries.

46 demonstrating the advantage of combining an ontology and a symptom-dependency-aware naive Bayes clas

47 We cross-linked EPIC with gene ontology and anatomy ontology terms, employing FDA metho

48 We cross-linked EPIC with gene ontology and anatomy ontology terms, employing Fisher's

49 splay potential biological functions in Gene Ontology and circRNA-miRNA-mRNA networks that are not ab

50 eview, we will compare and contrast existing ontology and data models, focusing on nonhuman phenotype

51 in biological process annotations from Gene Ontology and disease annotations from DisGeNET are captu

52 al adopters to improve specific areas of the ontology and extend standardized disease descriptions.

53 popular classification algorithms using gene ontology and gene expression datasets as features to pre

54 ll-known classification systems such as Gene Ontology and KEGG, but also offers additional, new class

55 nnotation and pathway databases such as Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes ar

56 e DMPs displayed enrichment of 58 and 6 Gene Ontology and Kyoto Encyclopedia of Genes and Genomes gen

57 Using gene ontology and network analysis, eight clusters of genes w

58 Gene ontology and network pathway analysis were performed usi

59 rine whole blood using RNAseq analysis, gene ontology and network topology-based key driver analysis.

60 Gene ontology and pathway analysis identified endoplasmic ret

61 Alternatively, gene ontology and pathway analysis in tissues indicated incre

62 pplied NetPAS in gene sets derived from gene ontology and pathway annotations and showed that NetPAS

63 Gene ontology and phenotype-genotype analysis suggested that

64 lines from NICR into the community-based CLO ontology and provides an integrative platform to support

65 We evaluated our ontology and rules by populating PGxO with knowledge uni

66 Our database also incorporated Gene Ontology and several pathway databases to enhance functi

67 users have access to historical data for the ontology and the annotations.

68 two new ontologies - the Kidney Tissue Atlas Ontology and the Ontology of Precision Medicine and Inve

69 egrated genotype-phenotype annotations, gene ontologies, and interaction networks to determine the cl

70 rformed using modular repertoires, IPA, Gene Ontology, and NetworkAnalyst 3.0.

71 value < 0.05), hierarchical clustering, gene ontology, and pathway representation.

72 Biological knowledge, and therefore Gene Ontology annotation sets, for human genes is incomplete.

73 g classification systems, such as Pfam, Gene Ontology annotation, mpstruc or the Transporter Classifi

74 To increase the utility of Gene Ontology annotations for interpretation of genome-wide e

75 trix and gene-term data matrix (storing Gene Ontology annotations of genes) into low-rank matrices to

76 Next, IsoFun uses the available Gene Ontology annotations of genes, gene-gene interactions an

77 s they were constructed based on the anatomy ontology annotations that were obtained from the experim

78 genes, and support for more expressive Gene Ontology annotations.

79 statistical associations, metadata terms and ontology annotations.

80 elationships with PPI networks using anatomy ontology annotations.

81 In this study, we used MS and a Gene Ontology approach to identify differentially sumoylated

82 Ontologies are an underexploited element of effective kn

83 Ontologies are critical to support the types of big data

84 Ontologies are powerful tools that facilitate these effo

85 nal activation and repression during NK cell ontology are poorly understood.

86 rolled vocabulary, the Antibiotic Resistance Ontology (ARO), designed by the CARD biocuration team to

87 ect to isotopic data, we propose an ISO-FOOD ontology as a domain ontology for describing isotopic da

88 ealed extracellular matrix organization Gene Ontology as the most significant.

89 ion network, hierarchal clustering, and gene-ontology based approaches identified a putative role for

90 they cannot be informed by representational ontology based on mental objects and states.

91 VOCCluster was created from a heuristic ontology based on the observation of experts undertaking

92 Integrated Gene Ontology-based analysis further revealed that ASD genes

93 d extract non-redundant associations of Gene Ontology-based functions.

94 rotein-metabolite network, we conduct a gene ontology-based semantic similarity ranking to find suita

95 31 of these genes were enriched in the gene ontology biologic process 'receptor-mediated endocytosis

96 se single isoform genes co-annotated to Gene Ontology biological process annotations, Kyoto Encyclope

97 expressed genes; the top five enriched gene ontology biological processes included (i) immune respon

98 First, we build an ontology by identifying meaningful entity mentions from

99 We define classes in Cancer Cell Ontology by using a genus-differentia approach using log

100 To show how such an ontology can be used in practise, it was populated with

101 ain development, enrichment in the same Gene Ontology categories as genes with mutations de novo in s

102 resent, though most significantly, that gene ontology categories relating to transcription are over-r

103 in select transcripts within these key gene ontology categories, underscoring the vulnerability of t

104 sembly, cell proliferation, and related Gene Ontology categories.

105 are statistically enriched for specific gene ontology categories.

106 These genes mapped to the following gene ontology categories: fatty acid degradation, peroxisome

107 Alterations due to MCS impact every gene ontology category queried, including GABAergic neurotran

108 wo new ontologies, including the Cancer Cell Ontology (CCL) and the Ontology for Stem Cell Investigat

109 The Cell Ontology (CL) and Cell Line Ontology (CLO) are two commu

110 on, intron/exon structural patterns and gene ontology classification as well as profile expression.

111 proteomic analysis (>=9.0 mL urine) and gene ontology classification identified 75% of the protein as

112 only present or more abundant in cas-c1 Gene ontology classification of these proteins identified pro

113 ards' derived from proteomic studies or Gene Ontology classifications.

114 The Cell Ontology (CL) and Cell Line Ontology (CLO) are two community-based OBO Foundry ontol

115 In another representation, the Cell Line Ontology (CLO) framework was applied and extended to rep

116 ell lines into the community-based Cell Line Ontology (CLO).

117 model organism databases (MODs) and the Gene Ontology Consortium (GO).

118 ow controlled vocabularies, thesauruses, and ontologies contribute to overcoming the streetlight effe

119 ative analysis of target prediction and Gene Ontology data.

120 22q11.2-like defect, Df(16)A(+/-) Molecular ontologies defined mitochondrial compartments and pathwa

121 age, disease, and other related factors, an ontology design pattern was developed and applied to gen

122 he general discussion session focused on the ontology design patterns in representing newly-discovere

123 We also proposed new ontology design patterns to represent the usage of cell

124 t be used generally to generate and evaluate ontology design patterns.

125 Combined with PROV-O, an ontology developed by the W3C to represent provenance in

126 nd has necessitated the development of a new ontology development environment called Tawny-OWL.

127 It packages common high-level ontology development functionality into a convenient lib

128 The ODAE development follows the OBO Foundry ontology development principles (e.g., openness and coll

129 library and command-line tool for automating ontology development tasks.

130 The Drug Ontology (DrOn) is a modular, extensible ontology of dru

131 The Evidence and Conclusion Ontology (ECO) contains terms (classes) that describe ty

132 In addition, we identify nodes in these gene ontology-enriched subnetworks that are coordinately cont

133 expression, functional annotation, and gene ontology enrichment analyses were performed.

134 Comparative gene ontology enrichment analysis evidenced that most stickli

135 Gene ontology enrichment analysis found that DEGs contributed

136 Gene ontology enrichment analysis of gene sets associated wit

137 Gene ontology enrichment analysis revealed immune-stimulatory

138 Gene ontology enrichment analysis revealed that OsHOX24 direc

139 Gene ontology enrichment analysis revealed that the dataset w

140 Gene ontology enrichment analysis revealed the promotion of g

141 transcriptome in B. distachyon, we used Gene Ontology enrichment analysis, and found several terms si

142 o identify regions (DMRs) and conducted gene ontology enrichment analysis.

143 Gene ontology enrichments revealed a large spectrum of ABA ac

144 se with avian immune gene evidence from Gene Ontology, Ensembl, UniProt and the B10K consortium has b

145 g review was conducted to: (1) identify what ontologies exist related to human behaviour change, (2)

146 is unclear whether a possible shared genetic ontology extends to the phenotype.

147 The resulting concepts can be formalized by ontologies for computational modelling, laying the found

148 we propose an ISO-FOOD ontology as a domain ontology for describing isotopic data within Food Scienc

149 In this paper, we introduce an application ontology for health information dialogue called Patient

150 e called Patient Health Information Dialogue Ontology for patient-level human papillomavirus vaccine

151 uding the Cancer Cell Ontology (CCL) and the Ontology for Stem Cell Investigations (OSCI), were repor

152 em cell investigations, we have developed an Ontology for Stem Cell Investigations (OSCI).

153 est were tested for enrichment in biological ontologies, for association with schizophrenia polygenic

154 Here, we derive a psychological ontology from a study of individual differences across a

155 n profiles, a total of 339,158 putative gene ontology functions associated with 1446 human m(6)A site

156 Systems biology approaches relying on gene ontologies, gene coexpression, and protein-protein inter

157 David database was then used to perform Gene ontology (GO) analysis and Kyoto Encyclopedia of Gene an

158 Gene ontology (GO) analysis highlighted genes relating to nuc

159 The gene ontology (GO) analysis implicated candidate genes under

160 Gene Ontology (GO) analysis of DEG that were higher or lower

161 Gene Ontology (GO) analysis of FIR up-regulated genes indicat

162 Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome

163 Gene Ontology (GO) and MapMan pathway analyses underlined tha

164 hways and functional categories such as gene ontology (GO) and other databases.

165 pproach to detect potentially incorrect Gene Ontology (GO) annotations by comparing the ratio of anno

166 r genes were significantly enriched for Gene Ontology (GO) categories mainly related to social behavi

167 od displaying a similar distribution of gene ontology (GO) cellular component assignments compared to

168 Clustering and gene ontology (GO) enrichment analyses of radish datasets fol

169 Moreover, gene ontology (GO) enrichment analysis of salt responsive tar

170 Gene ontology (GO) functional annotations analysis using Data

171 richment of genes and pathways based on Gene Ontology (GO) has been widely used to describe the resul

172 Gene ontology (GO) is an eminent knowledge base frequently us

173 The Gene Ontology (GO) is the unifying biological vocabulary for

174 elial cells uncovered the activation of Gene Ontology (GO) pathways relevant to the human disease, su

175 Based on the gene ontology (GO) term analysis, six promising candidate gen

176 Moreover, Gene Ontology (GO) term enrichment enabled identification of

177 tective (CyTP) genes categorized in the Gene Ontology (GO) term of "Xenobiotic Detoxification Program

178 port a number of significantly enriched gene ontology (GO) terms that are related to the cytoskeleton

179 on-redundant (nr) protein database, and Gene Ontology (GO) terms were assigned based on the top BLAST

180 ently, the tool provides annotations to Gene Ontology (GO) terms, and PANTHER family and subfamily.

181 lies and subfamilies are annotated with Gene Ontology (GO) terms, and sequences are assigned to PANTH

182 ignificantly (p < 0.05) enriched in 279 gene ontology (GO) terms, including those related to photosyn

183 associated with chondrogenesis-related gene ontology (GO) terms.

184 n propagation, and the structure of the Gene Ontology (GO) to best utilize sparse input labels and ma

185 oteomic resources like gene expression, Gene Ontology (GO), and protein-protein interaction network (

186 lly expressed genes (DEGs), followed by gene ontology (GO), Hallmark pathway enrichment and protein-p

187 n individual lipid names into multiple lipid ontology groups based on the classification (e.g. LipidM

188 statistically significant deviations in gene ontology groups can occur in seemingly high-quality asse

189 The HPO's interoperability with other ontologies has enabled it to be used to improve diagnost

190 Ontology has attracted substantial attention from both a

191 The new disorder ontology has been formalized and made interoperable by a

192 otypic similarities by using Human Phenotype Ontology (HPO) in 314 individuals with DEEs.

193 Here, we analyzed 31,742 Human Phenotype Ontology (HPO) terms in 846 individuals with existing wh

194 The Human Phenotype Ontology (HPO)-a standardized vocabulary of phenotypic a

195 Human Phenotype Ontology (HPO)-based analysis has become standard for ge

196 cept normalization using the Human Phenotype Ontology (HPO).

197 ntrons and exons in genomes without external ontologies, i.e., ab initio.

198 2018 International Conference on Biomedical Ontology (ICBO-2018), the 2nd International Workshop on

199 Within tasks and surveys, however, the ontology identifies reliable individual traits and revea

200 ional enrichment analyses revealed genes and ontologies implicated in glyoxylic acid-mediated ripenin

201 relationships with PPI networks via anatomy ontology improved the candidate gene prediction accuracy

202 doubling of protein entries, a new disorder ontology, improvements of the annotation format and a co

203 dards; ontologies (new Mondo unified disease ontology, improvements to ontologies such as HPO and uPh

204 Agreeing on and implementing ontologies in chemical assessment is a complex but tract

205 gy (CLO) are two community-based OBO Foundry ontologies in the domains of in vivo cells and in vitro

206 everaging the built-in logical axioms of the ontology in order to classify patient surface marker dat

207 s 2.0, the nodes of the acyclic hierarchical ontology include four antimicrobial compound types, 57 c

208 Two new ontologies, including the Cancer Cell Ontology (CCL) and

209 9 nearby genes which could be linked to gene ontology information.

210 We plan to integrate our ontology into existing tools for flow cytometry data ana

211 process branch of the GO biological process ontology is more challenging to represent than the cellu

212 A crucial aspect of Gilead and colleagues' ontology is the dichotomy between tangible and intangibl

213 herence as a construct, and that data-driven ontologies lay the groundwork for a cumulative psycholog

214 ms are the seed for the Microbial Conditions Ontology (MCO), a controlled and structured vocabulary t

215 Consistent with their unique ontology, microglia may express specific physiological m

216 enome-wide significant association of a Gene Ontology morphogenesis term (including assigned roles fo

217 r and YeastEnrichr are created from the Gene Ontology, mRNA expression profiles, GeneRIF, pathway dat

218 ions, that we believe Gilead and colleagues' ontology needs to incorporate.

219 es, better modeling); new API and standards; ontologies (new Mondo unified disease ontology, improvem

220 ign pattern and extending the OAE and NDF-RT ontology, ODAE has represented various AEs associated wi

221 of RNA transcripts without appealing to the ontology of "GT-AG" boundaries defining introns.

222 ntaries address our view of abstraction, our ontology of abstract entities, and our account of predic

223 Gilead et al. propose an ontology of abstract representations based on folk-psych

224 d to generate a community-driven open-source Ontology of Drug Adverse Events (ODAE).

225 rug Ontology (DrOn) is a modular, extensible ontology of drug products, their ingredients, and their

226 Within the ARA ontology of gray matter regions, TH-ir neurons localized

227 e host are represented in a machine-readable Ontology of Host-Pathogen Interactions.

228 s - the Kidney Tissue Atlas Ontology and the Ontology of Precision Medicine and Investigation - will

229 Notably, the ontology of the glomerulus coincides with induction of u

230 For Tomasello's proposed ontology of the human sense of moral obligation, observa

231 mesoderm (LPM)-like cells, the developmental ontology of the limb bud, or definitive endoderm.

232 ressed genes (DEGs) were categorized by gene ontology or Kyoto Encyclopedia of Genes and Genomes iden

233 Gene ontology overrepresentation analysis in P7SCI gene-sets

234 Gene ontology pathways mapped from altered proteins over time

235 une system and DNA integrity checkpoint gene ontology pathways were over-represented among the highes

236 ding the crowdsourced curation of biomedical ontologies, phenotype-based diagnosis of disease, and dr

237 Within the cancer domain, ontologies play an important role in the integration and

238 re we describe CANOPUS (class assignment and ontology prediction using mass spectrometry), a computat

239 ROBOT provides ontology processing commands for a variety of tasks, inc

240 pseudokinase sequences in the Protein Kinase Ontology (ProKinO) as a new mineable resource for the si

241 important resources of biological data (gene ontology, protein interaction data, protein sequence) al

242 Comparative Gene Ontology proteome analysis revealed that SUM52 cells wer

243 sed gene targets were also enriched for gene ontologies related to microtubule binding processes.

244 with controlled vocabulary from established ontologies - remains a challenge, especially when the ex

245 lso generated and deposited in the OntoChina ontology repository.

246 The review did not identify any ontologies representing the breadth and detail of human

247 on of the karyotype; the development of this ontology represents a significant advance from the tradi

248 Analysis of gene ontologies revealed that exosomes mirrored whole human l

249 In vitro transfection studies and gene ontology revealed involvement of these altered miRNAs in

250 These limitations in classification and ontology slow the discovery of pathophysiologic mechanis

251 Struct integrates with the STATistics Ontology (STATO) in order to ensure consistent reporting

252 ition, it leverages the PPI network and Gene Ontology structure to further coordinate the matrix fact

253 h International Workshop on Vaccine and Drug Ontology Studies (VDOS 2018), held on August 10th, 2018,

254 do unified disease ontology, improvements to ontologies such as HPO and uPheno); user interface (a re

255 opedia of Genes and Genomes pathway and Gene Ontology term enrichment analysis.

256 ogens was detected as the most enriched gene ontology term for the host D. scoparium, for those trans

257 sualization and enrichment analysis for gene ontology terms and pathways.

258 enes enriched for neurobiologically relevant ontology terms and pathways.

259 lim, containing over four times as many Gene Ontology terms as our previous GO-slim, as well as curat

260 n be annotated automatically with anatomical ontology terms by analyzing the spatial expression patte

261 The Gene Ontology terms cellular response to chemical stimulus, r

262 An enrichment analysis of gene ontology terms from each life stage or sex highlighted g

263 ated genes, several were categorized in gene ontology terms oxidation-reduction processes, ATP bindin

264 defense-related genes and enrichment of gene ontology terms related to immunity and salicylic acid re

265 h invasion (P = 0.03), was enriched for gene ontology terms relating to cell adhesion and migration,

266 with mathematical graphs based on anatomical ontology terms to automatically generate reference morph

267 to conduct enrichment analysis of the lipid ontology terms using a Shiny app with options of five st

268 s-linked EPIC with gene ontology and anatomy ontology terms, employing FDA methods to identify previo

269 s-linked EPIC with gene ontology and anatomy ontology terms, employing Fisher's Discriminant Analysis

270 y dosage balance-sensitive gene groups (Gene Ontology terms, metabolic networks, gene families, and p

271 By automatically associating relevant Gene Ontology terms, PALMER facilitates biological interpreta

272 ciations between gene descriptors (e.g. Gene Ontology terms, protein-protein interaction data and bio

273 opedia of Genes and Genome pathways and gene ontology terms.

274 lso present when function is defined by Gene Ontology terms.

275 phenotypes are mapped to Experimental Factor Ontology terms.

276 related genes, where the predictors are Gene Ontology terms.

277 distributions associated with different gene ontology terms.

278 individual lipids and their associated lipid ontology terms.

279 tical predictions based on 8 Human Phenotype Ontology terms.

280 alysis was used to generate and cluster Gene Ontology terms.

281 mechanistically link the two functional gene ontologies that have been implicated in human CHD: chrom

282 to publish metadata following standards and ontologies that make the public data available for reuse

283 rk will extend this framework to incorporate ontologies that provide pathway-level feature selection

284 We develop many widely adopted ontologies that together enable sophisticated computatio

285 With an ontology that represents patient-centric dialogue to com

286 The development of two new ontologies - the Kidney Tissue Atlas Ontology and the On

287 By gene ontology, the mouse and human ZGA genes with de novo PLA

288 eploys extensive controlled vocabularies and ontologies to annotate proteomics datasets.

289 ions included a report on the application of ontologies to cross-compare cell types and marker patter

290 eaver (GW), Reactome, and several biomedical ontologies to represent and compare common COVID-19 como

291 into the tranSMART i2b2 via a guiding master ontology tree positively impacts and supports the effica

292 We also built a new ontology, Tree of Human Body Parts (THBP), from core ana

293 framework to map anatomical entities to THBP ontology using normalization and a scoring algorithm bas

294 issue, we developed a Vaccine Investigation Ontology (VIO), and applied VIO to classify the differen

295 By adopting conventions used in the Cell Ontology, we have created human and computer-readable de

296 his article, we describe our use of OWL, the Ontology Web Language, to generate a fully computational

297 Methods used for developing the ontologies were expert consultation, data-driven techniq

298 These ontologies will improve the annotation of kidney-relevan

299 ion of EMR text data for enriching a medical ontology with probability information.

300 Successful implementation of ontologies would not only make currently fragmented info