ライフサイエンスコーパス: protein interaction network

1 dom Walk with Restart algorithm in a protein-protein interaction network.

2 the connectivity patterns of the underlying protein interaction network.

3 ly derived interactome data to build a RIG-I protein interaction network.

4 loci forming a highly interconnected protein-protein interaction network.

5 multiple species to form an innate immunity protein interaction network.

6 ity to functional assay results in a protein-protein interaction network.

7 are topologically central in a human protein-protein interaction network.

8 D cluster in discrete regions of the protein-protein interaction network.

9 ne function, biological pathway, and protein-protein interaction network.

10 s and allows a characterization of a protein-protein interaction network.

11 end to interact with each other in a protein-protein interaction network.

12 ing yeast (Saccharomyces cerevisiae) protein-protein interaction network.

13 logical information about the parent protein-protein interaction network.

14 a range of Rho GTPases using a novel protein-protein interaction network.

15 ogical characteristics of the parent protein-protein interaction network.

16 age discard rate of 45% on the yeast protein-protein interaction network.

17 ng selected signalling pathways from a human protein interaction network.

18 tomers and elucidated the underlying protein-protein interaction network.

19 eins that did not previously interact in the protein interaction network.

20 in a hierarchical scale-free fractal protein-protein interaction network.

21 Here, we investigated the PEN3 protein interaction network.

22 FF on analyzing gene expression on a protein-protein interaction network.

23 ns and closer with each other in the protein-protein interaction network.

24 ng no reported side effects in human protein-protein interaction networks.

25 ation for the structural characterization of protein interaction networks.

26 cies based solely on the topology of current protein interaction networks.

27 sets of proteins (MDSets) in human and yeast protein interaction networks.

28 re generally in hub intrinsically disordered protein interaction networks.

29 itoring structural changes and understanding protein interaction networks.

30 and frequently function as molecular hubs in protein interaction networks.

31 y of gene regulatory, metabolic, and protein-protein interaction networks.

32 l oncoproteins that hijack critical cellular protein interaction networks.

33 e of gene regulatory, metabolic, and protein-protein interaction networks.

34 provide a powerful tool for defining protein-protein interaction networks.

35 s, generating weighted gene-gene and protein-protein interaction networks.

36 teraction data for the bottom-up assembly of protein interaction networks.

37 le members of cellular signaling pathways or protein interaction networks.

38 sent an important factor in the evolution of protein interaction networks.

39 y different strategies for the generation of protein interaction networks.

40 iding a scalable approach to mapping protein-protein interaction networks.

41 onary pressure to develop scale-free protein-protein interaction networks.

42 erstand the notion of promiscuity in protein-protein interaction networks.

43 d perspective on the connectivity of protein-protein interaction networks.

44 free phenomenon that has been documented for protein interaction networks.

45 rnative source of information for generating protein interaction networks.

46 orch that quantifies connectivity in protein-protein interaction networks.

47 of better-characterized proteins in protein-protein interaction networks.

48 domains are critical for deciphering protein-protein interaction networks.

49 acteristics of living cells are regulated by protein interaction networks.

50 s an important mechanism in the evolution of protein interaction networks.

51 roteomic tool for the comprehensive study of protein interaction networks.

52 rm for the quantitative analysis of multiple protein interaction networks.

53 lists of genes and protein using background protein interaction networks.

54 from sequence profile alignments and protein-protein interaction networks.

55 factors and their placement within synaptic protein interaction networks.

56 formation while investigating the virus host protein interaction networks.

57 -wide association studies (GWAS) and protein-protein interaction networks.

58 ellular regulation and constitutively rewire protein interaction networks.

59 They often serve as hubs in protein interaction networks.

60 ometric analysis to visualization of protein-protein interaction networks.

61 roles in transcription or as hubs in protein-protein interaction networks.

62 hods have been developed to predict PPIs and protein interaction networks.

63 the effect of loss of function mutations in protein interaction networks.

64 ns are organized in highly connected protein-protein interaction networks.

65 ur method for reliable construction of ncRNA-protein interaction networks.

66 ular structure on transport in metabolic and protein-interaction networks.

67 cells, providing unprecedented insights into protein-interaction networks.

68 ocal neighborhood information in the protein-protein interaction network across successive ancestral

69 The ability to understand how protein interaction networks adapt to yield new function

70 A global protein-protein interaction network alignment algorithm attempts

71 Minimum dominating sets (MDSet) of protein interaction networks allow the control of underl

72 s an R package for use in both human protein-protein interaction network analyses and analyses of arb

73 Using protein-protein interaction network analyses, we identified mole

74 ene ontology enrichment analysis and protein-protein interaction network analysis are used to identif

75 Protein-protein interaction network analysis emphasised the role

76 By using protein interaction network analysis followed by functio

77 Protein interaction network analysis of all differential

78 Proximity labeling and protein interaction network analysis reveal that CPH1 fu

79 Protein interaction network analysis showed that dozens

80 Specifically, we used protein-protein interaction network analysis, structural modelin

81 ling an extensive, compartmentalized protein-protein interaction network analysis.

82 al annotation, pathway analysis, and protein-protein interaction network analysis.

83 al-world networks, including a large protein-protein interaction network and a large network of seman

84 te the functional diversity of the PLRV-host protein interaction network and demonstrate the usefulne

85 ed and SIV-infected hippocampus with a human protein interaction network and discover modules of gene

86 in, followed by alterations that rewired its protein interaction network and led to species-specific

87 Protein-protein interaction network and module analysis demonstr

88 Protein interaction network and pathway analysis reveale

89 e-wise P-values were superimposed on a human protein interaction network and searches were conducted

90 sonably robust with respect to errors in the protein interaction network and with respect to changes

91 essential genes, are more central in protein-protein interaction networks and are less likely to cont

92 tissues from TCGA, we derive sample-specific protein interaction networks and assign sample-specific

93 n-mass spectrometry methodology to elucidate protein interaction networks and co-complexes nucleated

94 des tools to generate and score both protein-protein interaction networks and coexpression networks.

95 chemical information through coupled protein-protein interaction networks and driven by the synthesis

96 ssue type-specific, gene co-expression based protein interaction networks and drug-target interaction

97 aluating the superenhancers quality, protein-protein interaction networks and enriched metabolic path

98 teractions will enhance our understanding of protein interaction networks and facilitate affinity mat

99 However, the comparative virus-host protein interaction networks and how these networks cont

100 ersity plays important roles in both protein-protein interaction networks and likely also in gene reg

101 Computational inference of protein interaction networks and protein complexes from

102 w IDPs to interact with multiple partners in protein interaction networks and provide important funct

103 r map can be used to refine existing protein-protein interaction networks and provides an important r

104 control SH2 domain-mediated cellular protein-protein interaction networks and suggest a new strategy

105 ld networks, from infrastructure networks to protein interaction networks and terrorist communication

106 predict the effects of specific mutations on protein interaction networks and the phenotypes they reg

107 ignatures in terms of metabolic pathways and protein interaction networks and to identify the genomic

108 known cellular pathways, and processes using protein interaction networks and topological analysis.

109 alized to genome-wide elucidation of protein-protein interaction networks and used for interaction pr

110 eins, their unique subcellular localization, protein-interaction network and diverse modes of activat

111 Brain collection, 2) a schizophrenia protein-protein interaction network, and 3) a systematic literat

112 in relational data, in the form of a protein-protein interaction network, and a hierarchically struct

113 rlaying association signals onto the protein-protein interaction network, and demonstrated it using s

114 unctional genomics data comprised of protein-protein interaction networks, and (4) a genome-wide expr

115 modules from gene expression data mapped on protein interaction networks, and a second one focused o

116 to study the rewiring of large-scale protein-protein interaction networks, and can be useful for func

117 lyses of RNA co-expression networks, protein-protein interaction networks, and gene regulatory networ

118 sing on the construction of physical protein-protein interaction networks, and highlighting approache

119 g data, GWASs, epigenomic profiling, protein-protein interaction networks, and standardized clinical

120 tivity and global position of a protein in a protein interaction network are known to correlate with

121 visiae, an organism in which high confidence protein interaction networks are available and synthetic

122 Protein-protein interaction networks are commonly sampled using

123 Genome-scale human protein-protein interaction networks are critical to understandi

124 Cellular protein interaction networks are integral to host defenc

125 Embedded within large-scale protein interaction networks are signaling pathways that

126 Protein-protein interactions networks are most often generated f

127 e illustrate the method with examples from a protein interaction network around epidermal growth fact

128 ph-theoretic properties of two proteins in a protein interaction network as input features for predic

129 We will describe the application of protein interaction networks as a translational approach

130 xt mining has been widely used in recreating protein interaction networks, as well as in detecting sm

131 ns: an N-terminal arm that forms an extended protein interaction network at the capsid interior, an S

132 roteasomal subcomplex establishes a specific protein interaction network at the upstream activating s

133 for optimizing global pairwise alignments of protein interaction networks, based on a local optimizat

134 o analyze and generate gene-gene and protein-protein interaction networks, based on both the user's o

135 Protein interaction network-based pathway analysis (PINB

136 Our observations demonstrate a novel protein-protein interaction network between GEP, ADAMTS-7/ADAMTS

137 and phenotypes in HPO based on human protein-protein interaction network, both DLP and tlDLP improved

138 omise for proteome-scale analysis of protein-protein interaction networks, but the technical challeng

139 It constructs a protein interaction network by adopting a knowledge-guid

140 In one, we disrupted the S. cerevisiae INO80 protein interaction network by isolating complexes after

141 l information is combined with known protein-protein interaction networks by a boosted tree regressio

142 ubject Headings) terms, pathways and protein-protein interaction networks by comparing identified tar

143 to readily perform data analyses of protein-protein interaction networks by using genetic and functi

144 Our analysis shows that protein interaction networks can be used to predict synt

145 Such approaches build on the assumption that protein interaction networks can be viewed as maps in wh

146 Therefore, protein interaction networks can elucidate the molecular

147 xome sequences using random-walk analysis of protein interaction networks, clinical relevance and cro

148 These checkpoints are governed by protein-interaction networks, composed of phase-specific

149 nalysis of the largest crosslinking-derived, protein interaction network comprising 1,391 crosslinked

150 evaluated in the context of a global protein-protein interaction network, constructed as part of this

151 This protein interaction network contains several proteins in

152 ts provide new evidence that the topology of protein interaction networks contains information about

153 ng a sample's gene expression profile with a protein interaction network, correlates with phenotypic

154 This indicates that protein interaction networks could plausibly be rich sou

155 of Gene Ontology annotation information and protein interaction network data.

156 xpert tools for chemogenomic, expression and protein interaction network data.

157 the Human Protein Reference Database protein-protein interaction network data.

158 Our experiments on protein-protein interaction networks demonstrate that our index

159 ection methods, which rely solely on protein-protein interaction networks derived from compounded dat

160 , we will focus on the recent development of protein interaction networks derived from quantitative p

161 ypothesize that in bacteria, the topology of protein interaction networks derived via co-conservation

162 ed on Cytoscape Web, for visualizing protein-protein interaction networks, differences in domain comp

163 n affinities, mathematical representation of protein interaction networks, discovery of protein compl

164 regulation associated with the remodeling of protein-interaction networks during neurogenesis, the mi

165 progression, and connect Pak to the complex protein interaction network enabling cell division.

166 glia and highlight an immune-related protein-protein interaction network enriched for previously iden

167 eterize duplication and divergence models of protein interaction network evolution.

168 ergence models are insufficient for modeling protein interaction network evolution.

169 ession profiles of the developing diaphragm, protein interaction networks expanded from the known CDH

170 f these techniques to study the evolution of protein interaction networks extends this analytical rig

171 anges in DNA methylation in the context of a protein interaction network, focusing on their topologic

172 How these proteins assemble to form a protein interaction network for caveolar morphogenesis i

173 rscore the utility of the membrane/signaling protein interaction network for gene discovery and hypot

174 raction screen that defined an mLANA protein-protein interaction network for lytic viral replication

175 To analyze the protein interaction networks for both Kae1 pathways, we

176 opens up the possibility to investigate drug-protein interaction networks for complete proteomes with

177 n the assembly and analysis of comprehensive protein interaction networks for lower eukaryotes.

178 The availability of large-scale protein-protein interaction networks for numerous organisms prov

179 ordingly, we examine the predictive power of protein interaction networks for synthetic genetic inter

180 We estimate the global gene or protein interaction networks for the disease and healthy

181 ectrometry-based method that generates a KAT protein interaction network from which we simultaneously

182 uces ever more network data, such as protein-protein interaction networks, gene regulatory networks a

183 Protein-protein interaction networks generated from kinome data

184 n yeast suggest that the topology of protein-protein interaction networks generated from physical int

185 the plausibility of interactions in protein-protein interaction networks given protein/gene expressi

186 networks, our bacteria co-conserved protein-protein interaction networks had scale-free topologies.

187 An extensive protein-protein interaction network has been identified between

188 topology of bacteria co-conservation protein-protein interaction networks has not previously been stu

189 Prediction of protein function from protein interaction networks has received attention in t

190 Protein-protein interaction networks have been used to evaluate

191 Internet, metabolic, air transportation and protein interaction networks, have distinct patterns of

192 data analysis within the context of protein-protein interaction networks, heatmaps or chord diagrams

193 y a high-confidence Drosophila Hippo protein-protein interaction network (Hippo-PPIN) consisting of 1

194 twork based approach and that the use of the protein interaction network improves the overall robustn

195 he phages Dp-1 and Cp-1 and their underlying protein interaction network in the host Streptococcus pn

196 organizes and maintains an extensive protein-protein interaction network in the nucleolus required fo

197 r complex formation and describe the protein-protein interaction network in which VirD4 is involved.

198 ut, quantitative characterization of protein-protein interaction networks in a fully defined extracel

199 We demonstrate the ability to characterize protein interaction networks in a modifiable environment

200 e provide reliable evidence that the size of protein interaction networks in different organisms appe

201 Our human TOP2B proximal protein interaction network included members of the cohe

202 suited for high-throughput screening of the protein interaction network ("interactome") on a genomic

203 rnative splicing is known to remodel protein-protein interaction networks ("interactomes"), yet large

204 Sen1p is embedded in a protein-protein interaction network involving direct binding to

205 that encode such memory effects, for generic protein interaction networks involving binary and unary

206 y control, we provide a scored human protein-protein interaction network (InWeb_InBioMap, or InWeb_IM

207 e correlation with centrality in the protein-protein interaction network is also seen in terms of the

208 For example, the protein-protein interaction network is the physical basis of mul

209 Functional module detection within protein interaction networks is a challenging problem du

210 The global alignment of protein interaction networks is a widely studied problem

211 Clustering of protein-protein interaction networks is one of the most common a

212 orks can be extrapolated to complete protein-protein interaction networks is unclear.

213 situation where a biological network, e.g. a protein interaction network, is in fact a subnetwork emb

214 Decentralization of the gene-protein interaction network may explain the relative pau

215 food webs, modules in biochemical networks (protein interaction networks, metabolic networks or gene

216 complex biological networks such as protein-protein interaction networks, metabolic networks, and re

217 udies and constructed an MI-specific protein-protein-interaction network (MIPIN).

218 tudy multicellular function in a multi-layer protein interaction network of 107 human tissues.

219 biotic resistance mechanisms, we analyse the protein interaction network of a multidrug-resistant A.

220 compare clusterings of a recently published protein interaction network of Arabidopsis thaliana.

221 egulating Cbln1, a key node in the expanding protein interaction network of autism genes.

222 he expression, ligand binding properties, or protein interaction network of GPR54.

223 Here, we describe a protein-protein interaction network of inositol polyphosphate-5-

224 Further analysis of the protein interaction network of RBPs associated with age

225 fication and mass spectrometry to define the protein interaction network of the beta-catenin destruct

226 Here we investigate the intracellular protein interaction network of the transmembrane ADAM12L

227 comprehensive and detailed assessment of the protein interaction network of the yeast 26S proteasome.

228 We generated a systematic protein-protein interaction network of virulence effectors from

229 We analyse the protein-protein interaction networks of five organisms, S. cerev

230 s to compare experimentally obtained protein-protein interaction networks of prokaryotes and eukaryot

231 uate SPICi's performance on several existing protein interaction networks of varying size, and compar

232 This network overlapped with protein-protein interaction networks on multiple measures and al

233 rain, we constructed two "in silico" protein-protein interaction networks, one with genes from any an

234 ow the core-scaffold machinery associates in protein-interaction networks or how proteins encoded by

235 ds.We present an expanded C. elegans protein-protein interaction network, or 'interactome' map, deriv

236 Cells operate through protein interaction networks organized in space and time

237 ng to our measure in different baker's yeast protein interaction networks, outperforming existing nod

238 a cell and the wiring of proteins in protein-protein interaction network (PIN) relates to their biolo

239 benchmark networks as well as 10 biological protein interaction networks (PINs).

240 integrates gene expression data and protein-protein interaction networks (PINs).

241 The protein-protein interaction network (PPIN)-based disease candida

242 Alignments of protein-protein interaction networks (PPIN) can be used to predi

243 Analysis of protein-protein interaction networks (PPINs) at the system level

244 Protein-protein interaction networks (PPINs) have been employed

245 Protein-protein interactions networks (PPINs) are known to share

246 Protein-protein interaction network prediction, coexpression dat

247 Protein interaction networks provide an important system

248 w to leverage these opportunities in protein-protein interaction networks related to several therapeu

249 d substrates is highly enriched for nodes in protein interaction networks, representing critical conn

250 Here we describe a protein interaction network required for extraneuronal g

251 sis studies demonstrate that entry into this protein interaction network requires the DNAJC14 C-termi

252 Proteomic analysis of the KEAP1 protein interaction network revealed a significant enric

253 ular, our analysis of an Arabidopsis protein-protein interaction network revealed that hub proteins w

254 c characterization, gene expression, protein-protein interaction networks, RT-PCR, and flow cytometry

255 heostat-like mechanism that alters the KNOX1 protein interaction network specifically during leaf dev

256 Biological protein interactions networks such as signal transductio

257 and antiphasic organization within a protein-protein interaction network, suggesting the existence of

258 rom phenotype similarity network and protein-protein interaction network, supervised by the label inf

259 Starting from a protein interaction network surrounding Sox2, we identif

260 on factors that form a highly interconnected protein interaction network surrounding the homeobox pro

261 roadly expressed, tend to be more central in protein interaction networks, tend to be more evolutiona

262 e OB-fold proteins form an extensive protein-protein interaction network that connects the two trimer

263 Boolean model of the glucocorticoid receptor protein interaction network that encapsulates functional

264 provide the first report of a host-pathogen protein interaction network that includes data-derived,

265 s of generated RNAP variants revealed an RNA/protein interaction network that is crucial for transcri

266 more, the RC and DNAJC14 reside as part of a protein interaction network that remains after 1% Triton

267 hods, it will be possible to assemble binary protein interaction networks that connect extracellular

268 ncorporate biological networks, e.g. protein-protein interaction networks that have recently been sho

269 ion networks allow the control of underlying protein interaction networks through their topological p

270 We observe profound changes in protein interaction networks throughout different stages

271 associations for all the genes in a protein-protein interaction network, tlDLP benefits from the enr

272 ate mechanism for the Beclin 1-Vps34 protein-protein interaction network to achieve precise control o

273 tion information in the context of the human protein interaction network to infer new phosphatase sub

274 at focus only on a few proteins toward whole protein interaction networks to describe the relationshi

275 e-wide association studies (GWASs) and human protein interaction networks to investigate whether a su

276 Protein-protein interaction networks, together with other biolog

277 is unbiased and scans a genome-wide protein-protein interaction network using a novel formulation fo

278 Secondly, we built a protein-protein interaction network using the InnateDB database.

279 GNET allows users to weight edges of protein-protein interaction networks using a logistic regression

280 ing Network Aligner) and apply it to protein-protein interaction networks using S 3 as the topologica

281 In a second approach, a protein interaction network was used to find associated

282 By integrating gene expression data with a protein interaction network we here demonstrate that can

283 ene expression dataset and the human protein-protein interaction network, we demonstrate the method l

284 By interfering with this protein interaction network, we have partially uncoupled

285 Indeed, we show that if protein interaction networks were random graphs, describ

286 ur analyses of public CRISPR screens suggest protein interaction networks, when integrated with gene

287 ith the other survival genes using a protein-protein interaction network, which identified clusters o

288 n BioDrugScreen originated from Human Cancer Protein Interaction Network, which we have updated, as w

289 sical binding sites based on the topology of protein interaction networks, which has recently shown t

290 red proteins and highly re-wired proteins in protein interaction networks, which have evolved new int

291 systems biology analysis of physical protein-protein interaction networks, which indicated convergenc

292 ility location" (RWFL) problem in a gene (or protein) interaction network, which differs from the sta

293 individual protein associations and complex protein interaction networks, while challenging, is an e

294 stems and the subsequent identification of a protein interaction network with a conserved role in inn

295 HR1, BOLA2, and GUCY1A3 are within a protein-protein interaction network with known PD genes.

296 se genes are reported to be within a protein-protein interaction network with PD genes and that they

297 a robust statistic (NetSig) that integrates protein interaction networks with data from 4,742 tumor

298 PhenomeExpress integrates protein interaction networks with known phenotype to gen

299 most topologically important hub in protein-protein interaction networks within the 16p11.2 region a

300 application for modeling information flow in protein interaction networks without prior restriction t