ライフサイエンスコーパス: network

1 direction of change depending on the type of network.

2 ifficult to decipher the p63 gene regulatory network.

3 was introduced in 2016 by the Wales Neonatal Network.

4 through the local connectivity of the street network.

5 NRF2, a master regulator of the antioxidant network.

6 geometry of the embryonic kidney epithelial network.

7 the ER on microtubules to generate a fine ER network.

8 dynamic stability within the primary visual network.

9 ing a single node of the oncogenic signaling network.

10 unction is integrated into the QS regulatory network.

11 discovered role of astrocytes within the SCN network.

12 ents diagnosed through the French Lymphopath network.

13 to be central 'nodes' or hubs of the fatigue network.

14 nectivity in the posterior temporal language network.

15 ically distributed representations in neural networks.

16 odulate the assembly of CD81-partner protein networks.

17 eloped by Savageau for analysing biochemical networks.

18 the discovery of complex and dynamic protein networks.

19 e hippocampus (HPC) and other memory storage networks.

20 ogical systems is encoded in gene regulatory networks.

21 bservation based on the structure of contact networks.

22 nes-in the context of memristor-based neural networks.

23 ional design of strand-displacement reaction networks.

24 ciated with decreased connectivity in select networks.

25 ly of highly ordered two-dimensional droplet networks.

26 gradient boosting model and two deep neural networks.

27 syndrome-specific and symptom-specific brain networks.

28 available reconstructed bacterial metabolic networks.

29 ke) to identify functionally connected brain networks.

30 ct general hospitals by operational delivery networks.

31 r opinion dynamics on scale-free and regular networks.

32 greater activity in attention-related brain networks.

33 chanisms driving complex mammalian signaling networks.

34 rmingling of distinct community transmission networks.

35 Biological networks across scales exhibit hierarchical organization

36 dle sheath cells, indicative of differential network activity across cell types.

37 s of testing the causal relationship between network activity and human task performance by systemati

38 monstrated a one-to-one relation between the network activity and the task performance from trial to

39 task performance and measuring corresponding network activity change.

40 ns of locomotor pattern and spinal locomotor network activity, likely resulting from defective surviv

41 portant in providing the necessary metabolic network activity.

42 ter interpretation, we show that our trained network ("AI-TAC") does so by rediscovering ab initio th

43 ly connected, in particular the sensorimotor network, also tend to be affected early during premanife

44 Network analysis also suggested differential IL-6-relate

45 Systems theories, including network analysis and machine learning, are well placed f

46 Furthermore, network analysis indicated an increase in necrotic cell

47 ysis that combines proteomics and regulatory network analysis infers the interaction between astrocyt

48 Gene co-expression network analysis methods have been widely used to identi

49 Disease (LOAD), we performed an integrative network analysis of multi-omics profiling of four cortic

50 Symptom network analysis was conducted to evaluate network and b

51 post-MI HF using weighted gene co-expression network analysis.

52 m network analysis was conducted to evaluate network and bridge centrality, and the network propertie

53 in by providing structural plasticity at the network and fiber level.

54 s for control of the local losses inside the network and of the violation of time-reversal symmetry v

55 ovement sleep after sleep deprivation at the network and single-cell level.

56 udying the relation between the 3D fibrillar network and the optical and mechanical properties of the

57 romatin of the affected and control cells as networks and analyze the network topology by state-of-th

58 namics of the pathogen lineages derived from networks and centrality metrics.

59 disease genes in protein-protein interaction networks and identified gene clusters with functional co

60 use, the mesoscale of pedestrian and bicycle networks and infrastructure such as Complete Streets pol

61 el, and the great potential to use tree-ring networks and results as a calibration target for next-ge

62 es through pathways that involve microtubule networks and the actin cytoskeleton.

63 cally focus on water-related low-cost sensor networks, and conceptualize them as socio-technical syst

64 d phase of Li(7) Ti(5) O(12) for Deep Neural Network applications is reported, given the large retent

65 e of Li(4) Ti(5) O(12) toward Spiking Neural Network applications, due to the shorter retention and l

66 t such tracking can be achieved by canonical network architectures and dynamics, and that the resulti

67 These networks are characterized by the existence of a univers

68 Artificial neural networks are notoriously power- and time-consuming when

69 ith a growing body of evidence that cortical networks are particularly vulnerable to mutations of Mec

70 Gene regulatory networks are typically constructed from gene expression

71 e text]-core decomposition of some empirical networks as well as that of some randomised counterparts

72 The activation of gene networks associated with adaptive immunity was linked to

73 ividual brain dynamics within discrete brain networks at high temporal resolution.

74 or nasal transcriptome profiling and applied network-based and probabilistic causal methods to identi

75 We develop an Individualized Network-based Co-Mutation (INCM) methodology by inspecti

76 approach the tick-host relationships using a network-based construct.

77 etic parameters through convolutional neural network-based image processing, including relative area

78 easures and has significant implications for network-based interventions.

79 ed a robust framework to predict how complex network behavior in DCvNs emerges from the chemical land

80 nriched in IBD, pointing to shared molecular networks between COVID-19 and IBD.

81 episodes (95% CI, 19.4%-21.7%) had an out-of-network bill.

82 d with a significantly higher risk of out-of-network bills, compared with episodes with no complicati

83 rategies through the Bat One Health Research Network (BOHRN).

84 op between peripheral inflammatory cells and networked brain regions involved in threat and reward pr

85 We have analyzed this allosteric network by means of ancestral sequence reconstruction (A

86 e the intensive computations of dense signed networks by providing upper and lower bounds, then solvi

87 e [Formula: see text]-shell structure of the networks can be accounted for by the community structure

88 owed lower activity in the cognitive control network (CCN) during the focus on breath condition in co

89 Play fighting network centrality was quantified using measures of indi

90 configurations of deep convolutional neural networks (CNNs) to localize and classify uptake patterns

91 he approach is based on convolutional neural networks (CNNs), which may be embedded in dedicated hard

92 This provides a network, complementary to the reach goal updater, that i

93 ictability of structures remains unclear, as networks' complex underlying formation dynamics are usua

94 f data to construct a multilayer interaction network composed of a gene regulatory layer, a protein-p

95 tions with higher frequencies in large-scale networks connecting anterior and posterior brain regions

96 Both variants have identical network connectivity and were compared to each other and

97 iving formation of structural and functional network connectivity during early development.

98 nd explore transient configurations of motor network connectivity in acute stroke.

99 model of cascade failure in weighted complex networks considering overloaded edges to describe the re

100 e enrichment of these genes in co-expression networks constructed from 10 human brain regions.

101 tudies show that widespread changes to brain networks contribute to cognitive dysfunction, and grey m

102 s of cell-type diversity and transcriptional networks controlling cell-fate specification.

103 d in human disease would allow the design of network-correcting therapies that treat the core disease

104 olidation mechanisms in hippocampal-cortical networks could account for spatial memory deficits.

105 le model involving the biocatalytic reaction network coupled with burst nucleation of nanoparticles a

106 Both CFS and PAC networks coupled theta and alpha oscillations with highe

107 nfer ASD risk operate in signal transduction networks critical for both cortical development and syna

108 explore the use of deep convolutional neural networks (DCNNs) to generate synthetic MRI ventilation s

109 Partial correlation networks demonstrated fluctuations in correlations betwe

110 It is becoming clear that alterations to HEV network density and/or morphology can result in immune a

111 hospital and a National Comprehensive Cancer Network-designated comprehensive cancer center) within o

112 onents of the photosynthesis gene regulatory network differentially accumulated between mesophyll and

113 Neural networks display the ability to transform forward-ordere

114 n area 7), a key hub within the default mode network (DMN) displays amyloid and tau-containing neurof

115 Deep neural networks (DNNs) have achieved state-of-the-art performan

116 Interestingly, the network does not undergo fusion.

117 "hub" or "leader" cells within the beta-cell network drive islet oscillations and that electrically s

118 to illuminate the features of this synaptic network due to the small size and dense packing of its e

119 uctural reorganisation of the transportation network during the study period.

120 The network dynamics underlying these phenomena are not full

121 istinct but complementary roles reflected in network dynamics.

122 asts strongly with intense brain-wide neural network dynamics.

123 fy small molecules that broadly correct gene networks dysregulated in a human induced pluripotent ste

124 Mapping the gene-regulatory networks dysregulated in human disease would allow the d

125 ecretome consists of a highly interconnected network enriched in RNA-binding proteins (RBPs) and EV c

126 We propose the COmpositional Zero-Inflated Network Estimation (COZINE) method for inference of micr

127 simulations that NExUS outperforms existing network estimation methods in this context, and apply it

128 t was associated with increased neuronal and network excitability after injury, including increased s

129 impaired performance resulting from aberrant network excitability in psychiatric and neurological dis

130 The network exhibits hallmarks of biochemical excitability:

131 DL models including the fully convolutional network (FCN), SegNet, Dilated-Net, original U-Net, and

132 phasizing their mutual dependence on genetic network features, fitness landscapes, and developmental

133 putational approach for predicting metabolic network fluxes, flux balance analysis, often uses biomas

134 malized daily carbon fluxes from the FLUXNET Network for 34 forest sites in North America, the season

135 Using United Network for Organ Sharing (UNOS) data, 14 844 HCC patien

136 Using United Network for Organ Sharing (UNOS) de-identified data from

137 ed for calculation of the CPRA by the United Network for Organ Sharing (UNOS), the OPTN contractor, h

138 tions can constrain this Arctic introduction network for species with different physiological limits,

139 ve immunity was linked to the suppression of networks for tight junction, gap junctional intercellula

140 This RGC network forms a new electrical channel combining the ON

141 The Ca(2+)-facilitated hydrogen-bonding network forms the structural basis of the unusual LH1 re

142 ble approach for inferring directed genotype networks from data, but also provide a unique insight in

143 hierarchical organization that may constrain network function.

144 Gene regulatory networks (GRNs) link transcription factors (TFs) to thei

145 specification is governed by gene regulatory networks (GRNs) that integrate the activity of signaling

146 sed on the interpretation of what the neural network has learned.

147 d primary) of stroke centre and telemedicine networks have been developed to coordinate the various s

148 ning methods, including convolutional neural networks, have enabled the development of AF screening p

149 olic and transport pathways, transcriptional networks, hormone signaling pathways, and plant developm

150 The HIV Prevention Trials Network (HPTN) 067/Alternative Dosing to Augment PrEP Pi

151 how these problems develop, the neuroimmune network hypothesis suggests that early-life stress initi

152 emory in extensive areas in the default mode network (i.e., greater task-induced deactivation) as wel

153 The AD-AE model consists of two neural networks: (i) an autoencoder to generate an embedding th

154 The structure of this network impacts not only the socioeconomic development o

155 Regulatory networks important for regeneration are constructed thro

156 hippocampus and a Posterior Medial cortical network in signaling event boundaries.

157 Deregulation of mitochondrial network in terminally differentiated cells contributes t

158 proach to untangle the dysregulated cellular network in the vicinity of pathogenic hallmarks of AD an

159 templates for patterning perfusable vascular networks in engineered tissues have been constrained in

160 The hierarchy of channel networks in landscapes displays features that are charac

161 cell-type- and cell-state-specific signaling networks in stem, Paneth, enteroendocrine, tuft and gobl

162 are predictive of changes in FOS correlation networks in the morphine-dependent state.

163 rly suitable for dissecting miRNA regulatory networks in vivo.

164 romandibular disorders exhibit altered brain networks in widespread cortical and subcortical regions.

165 Gene network inference and master regulator analysis (MRA) ha

166 The phenotypic network inferred via the Hill-Climbing algorithm was use

167 its enhanced power and frequency, and due to network interactions, activity in this excited frequency

168 eded only in subjects with lower spontaneous network interactions.

169 an take topological constraints of molecular networks into account and offer new perspectives than ex

170 ies integrate bidirectional recurrent neural networks into their models.

171 Link prediction in a complex network is a problem of fundamental interest in network

172 The central node of this network is G3BP1, which functions as a molecular switch

173 in rats and mice indicate that the INS-PSTN network is organized in a similar manner as the hyperdir

174 ncogenic mutations rewire regulatory-protein networks is important for rationalizing the mechanisms o

175 scriptionally regulated genes and associated networks is lacking.

176 certainty in the structure and parameters of networks is ubiquitous across computational biology.

177 The lacunocanalicular network (LCN) is a fluid-filled network structure, which

178 quired infections occurred across the UK ICU network linked with the first few years of a national in

179 This scientific commentary refers to 'Network localization of clinical, cognitive, and neurops

180 oNs, which we demonstrate on the London Rail Network (LRN).

181 Lesion network mapping (LNM) has been applied to true lesions (

182 Our study supports atrophy network mapping as a method to localize clinical, cognit

183 Here, we use a new technique termed 'atrophy network mapping' to test the hypothesis that single-subj

184 to three social categories: the self, social network members (including close others and acquaintance

185 k to think about the relationships among the network members.

186 This network meta-analysis found that meditation intervention

187 The present network meta-analysis suggests that, in comparison with

188 A frequentist network meta-analysis was conducted with a random-effect

189 p feature selection-based deep sparse neural network model (DNN-GFS) that is optimized for neoantigen

190 ditions using a combination of a biophysical network model of the inferior olive and a novel Bayesian

191 from a task-set learning experiment using a network model.

192 Finally, CA1 network modeling showed that desynchronized inputs can i

193 in this population utilizing novel graphical network models that can explicitly incorporate spatial i

194 Elastic network models were constructed to compare the dynamics

195 Using simulations with neural network models, we show that contemporary statistical me

196 suggest that SWIM analysis can identify key network modules related to complex diseases like COPD.

197 In this article, the international MCD network Neuro-MIG provides consensus recommendations to

198 ), Image Postprocessing, Informatics, Neural Networks, Neuro-Oncology, Oncology, Treatment Effects, T

199 Contributions to a network neuroscience understanding of status perception

200 We utilised the Neuroscience in Psychiatry Network (NSPN), a cohort of young people (aged 18-29 yea

201 Using a network of 41 populations of the amphibian host Rana pip

202 , and determined that a dynamically distinct network of 9 +/- 1 water molecules is present within the

203 fic local wiring patterns not only in the CS network of CLL cells, but also of healthy cells.

204 n photonics have sparked interest in using a network of coupled degenerate optical parametric oscilla

205 Despite having a network of cytoplasmic interconnections (plasmodesmata)

206 Results show that a network of differentially methylated regions in glucocor

207 applicability of the integration of a dense network of greenhouse gas sensors with a science-driven

208 the visual DVR that form the core of a dense network of highly specific connections between this regi

209 es show that the thin film is comprised of a network of intimately connected FAPI crystallites which

210 h pervades our bones and accommodates a cell network of osteocytes.

211 l conductivity-and the presence of a complex network of passive components that acts as a high-pass f

212 mobilizes a gene promoter through a dynamic network of polymeric nuclear actin.

213 is a member of the MYC transcription factor network of proteins that must heterodimerize with MYC-as

214 BA67 treatment downregulated expression of a network of transcription factors critical for chordoma s

215 Underlying this cell type patterning is a network of transcription factors including a central MYB

216 as consumers and decomposers in the trophic networks of Antarctic terrestrial and freshwater environ

217 The networks of Asians and Hispanics are sparse.

218 the structure and dynamics of the underlying networks of financial ecosystems.

219 Based on these promoters, regulatory networks of higher complexity are assembled, such as log

220 Thus, while orderly modular networks of orientation preference initially arise indep

221 were used to identify relevant transmission networks of the five most relevant HIV-1 types (B and ci

222 taining to the metabolic and gene regulatory networks operating in the glandular trichomes of N. taba

223 8, the Organ Procurement and Transplantation Network (OPTN) modified adult heart allocation to better

224 ishing a firm link between macro-scale brain network organisation and conscious cognition requires di

225 the strength of LH optogenetic responses and network oscillations, which were consistent with ultra-s

226 was quantified using measures of individual network position and entire network structure (degree, e

227 Overparametrized deep networks predict well, despite the lack of an explicit c

228 Our neural network predicts more accurate sub-compartment predictio

229 ially represented task identity while the MD network preferentially represented step-level informatio

230 rs; 66% women) who underwent surgery with in-network primary surgeons and facilities, 20.5% of episod

231 Case studies on four diseases show that network-principled drug combinations tend to have low to

232 Using a network propagation method, we ranked candidate genes by

233 e mechanistic links between the cellular and network properties of, and the computations performed by

234 luate network and bridge centrality, and the network properties were compared between the outbreak an

235 mission and integration, as well as emergent network properties.

236 tive, and neuropsychiatric symptoms to brain networks, providing insight into brain-behaviour relatio

237 xperiments indicate that the ensemble neural network reaches the average best area under the curve (A

238 val) fell primarily within canonical default network regions.

239 Both networks represented the content and position of individ

240 g to the different functional modules of the network revealed that 76% of the genes and all gene modu

241 Chemokine redundancy and ensuing network robustness has frustrated therapeutic developmen

242 However, whether such resting-state networks (RSNs) are interconnected across the brain and

243 cal connectivity with cortical resting-state networks (RSNs) in awake marmosets using resting-state f

244 work is a problem of fundamental interest in network science and has attracted increasing attention i

245 h experiments and simulations on the dynamic network self-assembly and subsequent collapse of the syn

246 thods in this context, and apply it to learn network similarity and shared pathway activity for group

247 We present a global time series of street-network sprawl-that is, sprawl as measured through the l

248 Foaming ability, foam stability and gel network stability increased upon frozen storage due to p

249 Gleason score, National Comprehensive Cancer Network stage, PSA level, PSA doubling time, PSA velocit

250 es of individual network position and entire network structure (degree, eigenvector, betweenness, clu

251 cies richness was unaffected by fertilisers, network structure changed significantly as the replaceme

252 the sample size, suggesting that the optimal network structure is data-driven, not sample size driven

253 Furthermore, the optimal network structure was mostly determined by the data natu

254 These membranes have a multimodal porous network structure with tunable surface charge, pore size

255 ued shifts in interactions appear to reshape network structure, leading to dramatic community changes

256 ocanalicular network (LCN) is a fluid-filled network structure, which pervades our bones and accommod

257 The dynamics of theorized network structures called ring attractors elegantly acco

258 nalysis reveals multiple evolutionary stable network structures that depend on the availability of po

259 from the DIAN (Dominant Inherited Alzheimer Network) study group cohort with amyloid positron emissi

260 re in important topological positions in the network, suggesting relationships between the bloom domi

261 We further investigated the molecular network supporting the CCB system and found that CCB col

262 f transactions that would make the Lightning Network sustainable for a given level of fees or, altern

263 ding retrograde transport to the trans-Golgi network (TGN), is involved in the presentation of ligand

264 rality captures more complicated dynamics on networks than traditional centrality measures and has si

265 gene expression(1), but the gene regulatory network that controls oocyte growth remains unknown.

266 work proposes DeepH3, a deep residual neural network that learns to predict inter-residue distances a

267 unfolded protein response (UPR)-a signaling network that ultimately determines cell fate.

268 ave enabled mapping of the complex molecular networks that govern cellular behavior.

269 tomic findings of miR-128 in regulating gene networks that govern membrane excitability.

270 outcomes of vast, interconnected regulatory networks that influence cell fates and lineage commitmen

271 eomics phenotypes and identifies correlative networks that may eventually be targeted in a personaliz

272 mprehensive understanding of the interactome networks that mediate genotype-phenotype relationships(1

273 to the model-driven design of synthetic gene networks, the fast and portable sensing of compounds, on

274 istributes oxidative stress to sensitize the network to mitochondrial criticality.

275 ategically expanding the existing global MPA network to protect an additional 5% of the ocean could i

276 pensive, and can be integrated into a larger network to provide enhanced spatial and temporal coverag

277 We trained the convolutional neural network to segment six major renal structures: glomerula

278 vised algorithms that can reconstruct kinase network topologies from these phosphoproteomics data.

279 nd control cells as networks and analyze the network topology by state-of-the-art methods.

280 composition and regulation of the signalling network underlying the cytosolic calcium fluctuations ar

281 duced interaction of CG with the microtubule network upon a3-subunit knockdown.

282 The network was implemented in the MATLAB toolbox CellNetAna

283 dential and nonresidential), roads, and pipe networks (wastewater, water supply, and natural gas).

284 on and interconnection within the eicosanoid network, we hypothesized that 12/15-LOX is also active d

285 In the highly interconnected network, we identify pathway communities and hundreds of

286 of O2 carrier interaction in tumor capillary networks, we accounted for factors such as non-uniform v

287 Investigating the brain connectivity networks, we successfully identified a robust and reprod

288 ions comprising a previously defined spatial network were evaluated.

289 he local environmental information where the networks were sampled.

290 Three-dimensional convolutional neural networks were trained to estimate global visual field in

291 Neural networks were trained to segment organs in PET/CT acquis

292 n (COZINE) method for inference of microbial networks which addresses these critical aspects of the d

293 time scales at different regions of the flow network, which can be classified into flowing and stagna

294 Precipitation from ethanol resulted in a network with a highly organized, porous structure of inc

295 which uses a bidirectional recurrent neural network with long short-term memory (BLSTM) to capture t

296 nificantly associated on protein interaction networks with the differences in transcript levels betwe

297 g resting-state fMRI and then compared these networks with those in humans using connectivity fingerp

298 Mapping intercellular communication networks within the heart, we identified key intercellul

299 xpressed chromatin regulators modulate these networks, yet the mechanisms governing how tissue specif

300 In the presence of the dimeric protein, the network yields a cooperative supramolecular assembly wit