コーパス検索結果 (１語後でソート)
通し番号をクリックするとPubMedの該当ページを表示します
1 th a hierarchical structure derived from the Gene Ontology.
2 ubset of genes enriched for the inflammation gene ontology.
3 d by depth of proteins within hierarchies of gene ontologies.
4 were extracted from the transcriptomes using Gene Ontology, adult-brain gene lists generated by Trans
25 e the function of mitochondria, according to gene ontology analysis of proteins that are down-regulat
46 ance and stress fiber formation by TGF-beta, gene ontology analysis showed that genes encoding extrac
48 ghted gene coexpression network analysis and gene ontology analysis to identify biological processes
51 proteins and 10 autoantigens identified from gene ontology analysis were combined with 48 proteins id
52 sion analysis, principal component analysis, gene ontology analysis, and network analysis) or multipl
58 ch display potential biological functions in Gene Ontology and circRNA-miRNA-mRNA networks that are n
59 rends in biological process annotations from Gene Ontology and disease annotations from DisGeNET are
60 five popular classification algorithms using gene ontology and gene expression datasets as features t
61 bility to predict biological functions using Gene Ontology and gene-disease associations using Human
64 ts well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new
65 ene annotation and pathway databases such as Gene Ontology and Kyoto Encyclopaedia of Genes and Genom
67 involved in the regulation of miR-124-3p by Gene Ontology and Kyoto Encyclopedia of Genes and Genome
68 these DMPs displayed enrichment of 58 and 6 Gene Ontology and Kyoto Encyclopedia of Genes and Genome
72 in murine whole blood using RNAseq analysis, gene ontology and network topology-based key driver anal
76 lso applied NetPAS in gene sets derived from gene ontology and pathway annotations and showed that Ne
77 t into the function of the DEGs, we examined gene ontology and phenotype enrichment and found signifi
81 a reference-transcriptome-guided approach on gene ontology and tox-pathways, we confirmed the novel a
82 d integrated genotype-phenotype annotations, gene ontologies, and interaction networks to determine t
83 fferentially expressed transcripts, enriched gene ontology, and altered functions and canonical pathw
86 analyses with differential gene expression, gene ontology, and weighted gene correlation network ana
87 odelling and protein catabolism according to Gene Ontology; and highlight the opportunities for provi
89 ctable in the circulation, we also created a gene ontology annotation for circulating miRNAs using th
91 using classification systems, such as Pfam, Gene Ontology annotation, mpstruc or the Transporter Cla
93 ta matrix and gene-term data matrix (storing Gene Ontology annotations of genes) into low-rank matric
95 functional pairs (negative pair), we use the Gene Ontology annotations tagged with "NOT" qualifier.
96 typic information of individual samples with gene ontology annotations to derive a ranking of genes a
97 positive and negative class labels, updated Gene Ontology annotations, and by literature evidence.
103 pression network, hierarchal clustering, and gene-ontology based approaches identified a putative rol
106 his protein-metabolite network, we conduct a gene ontology-based semantic similarity ranking to find
109 enes; 31 of these genes were enriched in the gene ontology biologic process 'receptor-mediated endocy
111 we use single isoform genes co-annotated to Gene Ontology biological process annotations, Kyoto Ency
113 With the generated signaling network and gene ontology biological process term grouping, we ident
114 ially expressed genes; the top five enriched gene ontology biological processes included (i) immune r
117 s were enriched in the biological process of gene ontology categories and at later treatment stages.
118 or brain development, enrichment in the same Gene Ontology categories as genes with mutations de novo
119 se populations in genes enriched for several gene ontology categories related to muscle adaptation, c
120 are present, though most significantly, that gene ontology categories relating to transcription are o
122 identified subnetworks that were enriched in gene ontology categories, revealing directional regulato
123 found in select transcripts within these key gene ontology categories, underscoring the vulnerability
129 r-representation of L1 insertions within the gene ontologies 'cell projection' and 'postsynaptic memb
131 ication, intron/exon structural patterns and gene ontology classification as well as profile expressi
132 for proteomic analysis (>=9.0 mL urine) and gene ontology classification identified 75% of the prote
134 are only present or more abundant in cas-c1 Gene ontology classification of these proteins identifie
136 le in their degradation by XRN4 and VCS, and Gene Ontology clustering revealed novel actors of seed d
139 multiple ontology categories (like pathways, gene ontology, disease categories) and therefore expedit
140 In addition, we identify nodes in these gene ontology-enriched subnetworks that are coordinately
142 of the detected proteins was assessed using Gene Ontology enrichment analysis and Ingenuity Pathway
147 th a high similarity to Arabidopsis APETALA1 Gene Ontology enrichment analysis of differentially expr
151 th immunochemical confirmation combined with gene ontology enrichment analysis revealed that ERp57 ta
155 clic transcriptome in B. distachyon, we used Gene Ontology enrichment analysis, and found several ter
156 time course expression profiles, clustering, gene ontology enrichment analysis, differential expressi
157 erenhancer signal profile, associated genes, gene ontology enrichment analysis, motifs of transcripti
161 idation tests (Edge Set Enrichment Analysis, Gene Ontology Enrichment, Disease-Gene Subnetwork Compac
162 ved in the wound-healing process, as well as Gene Ontology enrichments in fundamental wound-healing p
164 controlled Ikaros over-expression to recover gene ontology enrichments, identify motifs in genomic re
165 atabase with avian immune gene evidence from Gene Ontology, Ensembl, UniProt and the B10K consortium
166 ee categories of protein functions including gene ontology, enzyme commission and ligand-binding site
168 e ontologies, with the interferon-associated gene ontology exhibiting the highest percentage of upreg
170 lation profiles, a total of 339,158 putative gene ontology functions associated with 1446 human m(6)A
171 unigenes, differentially expressed unigenes, gene ontology functions, and the family of peroxidase ge
174 ion data with external sources of orthology, gene ontology, gene interaction and pathway information.
176 chemokine ligand 4 (CCL4), while exploratory gene ontology (GO) analyses revealed lower expression of
178 The David database was then used to perform Gene ontology (GO) analysis and Kyoto Encyclopedia of Ge
189 new approach to detect potentially incorrect Gene Ontology (GO) annotations by comparing the ratio of
190 ailable research about synapse biology using Gene Ontology (GO) annotations to novel ontology terms:
191 structed protein sequence, a set of inferred Gene Ontology (GO) annotations, and a 'proxy gene' for e
192 icals/drugs, genes/proteins, diseases, taxa, Gene Ontology (GO) annotations, pathways, and gene inter
193 rward deep neural networks, as a solution to Gene Ontology (GO) based protein function prediction.
194 utlier genes were significantly enriched for Gene Ontology (GO) categories mainly related to social b
195 method displaying a similar distribution of gene ontology (GO) cellular component assignments compar
196 ntegrating the hierarchical structure of the Gene Ontology (GO) data dramatically improves prediction
202 howed same effect directions in stage-2, the gene ontology (GO) enrichment analysis showed several si
205 al enrichment of genes and pathways based on Gene Ontology (GO) has been widely used to describe the
211 well-defined structure and manual curation, Gene Ontology (GO) is the most frequently used vocabular
213 ndothelial cells uncovered the activation of Gene Ontology (GO) pathways relevant to the human diseas
214 s of treated and untreated C. albicans using Gene Ontology (GO) revealed a large cluster of down regu
217 toprotective (CyTP) genes categorized in the Gene Ontology (GO) term of "Xenobiotic Detoxification Pr
218 We report a number of significantly enriched gene ontology (GO) terms that are related to the cytoske
219 performing predictors proposed recently use gene ontology (GO) terms to construct feature vectors fo
220 the non-redundant (nr) protein database, and Gene Ontology (GO) terms were assigned based on the top
222 Currently, the tool provides annotations to Gene Ontology (GO) terms, and PANTHER family and subfami
223 families and subfamilies are annotated with Gene Ontology (GO) terms, and sequences are assigned to
224 detecting genomic features, here defined by gene ontology (GO) terms, enriched for causal variants a
226 ere significantly (p < 0.05) enriched in 279 gene ontology (GO) terms, including those related to pho
231 nction propagation, and the structure of the Gene Ontology (GO) to best utilize sparse input labels a
232 nd proteomic resources like gene expression, Gene Ontology (GO), and protein-protein interaction netw
233 entially expressed genes (DEGs), followed by gene ontology (GO), Hallmark pathway enrichment and prot
235 Differential gene expression (fold-change), gene ontology (GO; biological process) and pathway analy
236 hese statistically significant deviations in gene ontology groups can occur in seemingly high-quality
239 gh-density microarrays and pathway analyses (Gene Ontology, Kyoto Encyclopedia of Genes and Genomes,
240 urately classify HRGPs leads to inconsistent gene ontologies limiting the identification of HRGP clas
241 and colon organoids, along with RNA-Seq and gene ontology methods, to characterize the effects of IL
242 d a genome-wide significant association of a Gene Ontology morphogenesis term (including assigned rol
243 nrichr and YeastEnrichr are created from the Gene Ontology, mRNA expression profiles, GeneRIF, pathwa
246 y expressed genes (DEGs) were categorized by gene ontology or Kyoto Encyclopedia of Genes and Genomes
250 , immune system and DNA integrity checkpoint gene ontology pathways were over-represented among the h
253 nrichment analysis revealed common pathways, gene ontology, protein domains, and cell type-specific e
254 hree important resources of biological data (gene ontology, protein interaction data, protein sequenc
257 xpressed gene targets were also enriched for gene ontologies related to microtubule binding processes
260 n addition, it leverages the PPI network and Gene Ontology structure to further coordinate the matrix
261 ion of COI1 and enrichment of genes with the Gene Ontology term 'cullin-RING ubiquitin ligase complex
265 annotation for circulating miRNAs using the gene ontology term extracellular space as part of blood
266 onitrogens was detected as the most enriched gene ontology term for the host D. scoparium, for those
271 functional information, we are disseminating Gene Ontology terms annotated against miRBase sequences.
273 GO-slim, containing over four times as many Gene Ontology terms as our previous GO-slim, as well as
276 regulated genes, several were categorized in gene ontology terms oxidation-reduction processes, ATP b
277 eral defense-related genes and enrichment of gene ontology terms related to immunity and salicylic ac
278 tion has identified a large number of unique gene ontology terms related to metabolic activities, a r
280 d with invasion (P = 0.03), was enriched for gene ontology terms relating to cell adhesion and migrat
282 annotations to derive a ranking of genes and gene ontology terms using a supervised learning approach
283 tegorized into 27 functional groups based on Gene Ontology terms, including 14 groups in biological p
284 tively dosage balance-sensitive gene groups (Gene Ontology terms, metabolic networks, gene families,
287 associations between gene descriptors (e.g. Gene Ontology terms, protein-protein interaction data an
297 ings mechanistically link the two functional gene ontologies that have been implicated in human CHD:
299 y leading biological resources including the Gene Ontology, UniProt and several model organism databa
300 the upregulation of multiple proinflammatory gene ontologies, with the interferon-associated gene ont