ライフサイエンスコーパス: graph

1 eoretical causal framework (Directed Acyclic Graph).

2 ical connectivity but nonidentical molecular graphs).

3 an capture such neighborhood dependency in a graph.

4 ighborhood relationships encoded by a sparse graph.

5 ge orientation with respect to the hubs of a graph.

6 ses the distance index to cluster seeds on a graph.

7 l drugs and targets in the created knowledge graph.

8 sualization of the learned causal (directed) graph.

9 represented graphically as a signed directed graph.

10 of propagation away from the hub core of the graph.

11 N using both the skip graph and the original graph.

12 ning on the multidimensional measures of the graph.

13 t model, produces a growing directed acyclic graph.

14 spectrum of a disease in a single connected graph.

15 egative sampling of nonadjacent nodes in the graph.

16 olkit (VG) represents genetic variation as a graph.

17 genomes become much more difficult in genome graphs.

18 leotide and amino acid sequences to assembly graphs.

19 tion of mapping algorithms based upon genome graphs.

20 commonly modeled as static undirected signed graphs.

21 weighted, hypergraphs, rather than merely as graphs.

22 lation, or chaos by virtue of their reaction graphs.

23 nition generalizes Ollivier's definition for graphs.

24 networks) implemented as node/ edge-coloured graphs.

25 inadequate in reducing overfitting on sparse graphs.

26 ge extraction, orthology search and powerful graph algorithms to create navigable pathways tailored t

27 Graph algorithms, in turn, allow to infer likely paths o

28 AR uses an NGS-derived breakpoint graph alongside OM scaffolds to produce high-fidelity re

29 networks between IBS patients and HCs using graph analysis in two independent cohorts.

30 n RDKit and NetworkX, SG integrates scaffold graph analysis into the growing scientific/cheminformati

31 We used a weighted graph analysis of the adjacency matrix based on partial

32 erve more attention in the future biomedical graph analysis.

33 een packages were assessed through a network graph analysis.

34 GCNG encodes the spatial information as a graph and combines it with expression data using supervi

35 hat we can efficiently construct a pangenome graph and compactly encode tens of thousands of structur

36 thin an image onto a global information-rich graph and machine learning on the multidimensional measu

37 a logic-based ODE model from the interaction graph and outputs the model validation percent.

38 eme that optimizes a GNN using both the skip graph and the original graph.

39 graph to explore the overall topology of the graph and to predict the phenotype/clinical outcome of p

40 distribution that efficiently samples sparse graphs and (ii) the relaxation of common restrictive mod

41 data, continuous data, bar graphs, side bar graphs and data that describes relationships between sam

42 our work to recent approaches for clustering graphs and detecting communities in networks.

43 ows can be used to generate: (i) interactive graphs and tables providing comprehensive annotation and

44 ut two heterogeneous networks (node-coloured graphs) and builds a local alignment of them.

45 er, such methods require an existing feature graph, and potential mis-specification of the feature gr

46 embly, filters and disentangles the assembly graph, and produces all possible configurations of circu

47 ll properly two-colored, undirected, regular graphs are evolutionarily equivalent (where "properly co

48 erred gene trees and ancestral recombination graphs (ARGs).

49 attan plot in three dimensions, wrapping the graph around the user in a simulated cylindrical room.

50 These results show random graphs as a promising model to capture structural differ

51 We show that variation graphs, as implemented in the vg toolkit, provide an eff

52 A novel technique, adapted to these graphs, assessed global regularity of signal intensity p

53 egy that models the residue environment as a graph at the atomic level.

54 Here, we introduce signed variational graph auto-encoder (S-VGAE), an improved graph represent

55 embedding methods, hierarchical variational graph auto-encoder learns more informative and generaliz

56 , we have developed hierarchical variational graph auto-encoders trained end-to-end to jointly embed

57 In addition, we use a graph autoencoder consisting of graph convolutional laye

58 We present a novel pedigree sequence graph based approach to diploid assembly using accurate

59 rpins SemMedDB, a literature-scale knowledge graph based on semantic relations.

60 les from the data space and builds a network graph based on the data topology.

61 In this paper, we present ppiGReMLIN, a graph based strategy to infer interaction patterns in a

62 m morphologies are encoded with mathematical graphs based on anatomical ontology terms to automatical

63 tween these cell types were computed using a graph-based algorithm (topological tumor graphs, TTG).

64 We developed TooManyCells, a suite of graph-based algorithms for efficient and unbiased identi

65 We describe the linear framework, a graph-based approach to Markov processes, and show that

66 l facilitate accelerating the development of graph-based approaches in solving sequence to genome ass

67 OCT images were analyzed using the in-built graph-based automatic segmentation algorithm for single

68 ssed read loss during mapping and designed a graph-based classifier, VAPOR, for selecting mapping ref

69 Using a graph-based clustering algorithm, we found that certain

70 We present SVXplorer, which uses a graph-based clustering approach streamlined by the integ

71 n matrix and identifies cell clusters with a graph-based clustering strategy.

72 , we have developed BBKNN, an extremely fast graph-based data integration algorithm.

73 The flexible graph-based formalism of the CGP map can be easily gener

74 Here we present Corticall, a graph-based method that combines the advantages of multi

75 Here, we introduce Panaroo, a graph-based pangenome clustering tool that is able to ac

76 Graph-based representation of genome assemblies has been

77 s of the search are displayed using the same graph-based representation of the pattern.

78 We proposed Graph-based Residue neighborhood Strategy to Predict bin

79 Here, we have shown that by using our graph-based signatures and atomic interaction informatio

80 We previously showed that graph-based signatures can be used to predict the effect

81 lished mutation modelling approach that uses graph-based signatures to model protein geometry and phy

82 We introduce the notion of graph-based structural pattern (GSP) as an abstract mode

83 vo assemblies improve genotyping compared to graphs built from intermediate SV catalogs in the VCF fo

84 is then transformed into weighted bipartite graph by considering the interaction strength between th

85 d potential mis-specification of the feature graph can be harmful on classification and feature selec

86 feature graph extractor, so that the feature graph can be learned in a supervised manner and specific

87 A wide variety of graphs can be prepared in TBtools using a new plotting e

88 All generated graphs can be saved to local machines, and tables can be

89 Maps and graphs can operate simultaneously or separately, and the

90 th enumeration, epidemic models and standard graph centrality measures.

91 ctures, identified by graph connectivity and graph coloring, are evolutionarily equivalent.

92 ion, analysis, and visualization of vascular graphs composed of over 100 million vessel segments.

93 Applying a novel genome graph computational paradigm to analyze the topology of

94 e address is which structures, identified by graph connectivity and graph coloring, are evolutionaril

95 d multiple reference data sources to restore graph connectivity information, and call variants using

96 ive theories suggest that they are cognitive graphs consisting of locations connected by paths.

97 ssemblies from 12 yeast strains to show that graphs constructed directly from aligned de novo assembl

98 PASTIS a high-performance protein similarity graph construction pipeline.

99 mulated and real aDNA samples to a variation graph containing 1000 Genome Project variants and compar

100 mple in a linear genome but complicated in a graph context.

101 Unlike conventional graph convolution networks always assuming the same node

102 Typically we are interested in why and how graph convolution networks can help in drug-related task

103 nterdomain information fusion with bipartite graph convolution operation.

104 Here, we present a graph convolutional deep neural network (DNN) model, tra

105 on, we use a graph autoencoder consisting of graph convolutional layers to predict relationships betw

106 onvolutional network consisting of a uniform graph convolutional network and multiple subnetworks.

107 Specifically, DeepCDR is a hybrid graph convolutional network consisting of a uniform grap

108 In this work, we introduce ChromeGCN, a graph convolutional network for chromatin profile predic

109 Graph convolutional networks (GCN) can capture such neig

110 introduce the theoretical foundations behind graph convolutional networks and illustrate various arch

111 a systematic review on the emerging field of graph convolutional networks and their applications in d

112 llenges and future possibilities of applying graph convolutional networks to drug discovery.

113 brid machine learning approach consisting of graph convolutional networks used to extract molecular s

114 To achieve this, we developed Graph Convolutional Neural networks for Genes (GCNG).

115 ple residues in spatial proximity, we employ graph convolutions to aggregate properties across local

116 By applying graph convolutions to this explicit molecular representa

117 data aggregation model, such as a knowledge graph, could provide a common foundation for the study o

118 d connected organization as directed acyclic graphs (DAGs) and the lack of tools allowing to exploit

119 It uses a tensor product graph data integration and diffusion procedure to reduce

120 Clustering of tumors according to genome graph-derived features identified subgroups associated w

121 is chemical code can then be combined with a graph describing the hardware modules and compiled into

122 Integrative network analysis using graph diffusion and multitask clustering of FMR1 CLIP-se

123 ssNPA learns a causal graph directly from control data.

124 mmendation system are presented by ORSO in a graph display, allowing exploration of dataset associati

125 riments, participants learned three abstract graphs during two successive days.

126 g on external knowledge, we propose a forest graph-embedded deep feedforward network (forgeNet) model

127 ps between the biological units, such as the graph-embedded deep feedforward network (GEDFN) model, h

128 ent Identifier (SCI), an algorithm that uses graph embedding followed by unsupervised learning to pre

129 d to systematically evaluate the more recent graph embedding methods (e.g. random walk-based and neur

130 and protein function predictions, the recent graph embedding methods achieve competitive performance

131 rimental results demonstrate that the recent graph embedding methods achieve promising results and de

132 We select 11 representative graph embedding methods and conduct a systematic compari

133 de general guidelines for properly selecting graph embedding methods and setting their hyper-paramete

134 To date, most recent graph embedding methods are evaluated on social and info

135 actorization (which can be seen as a type of graph embedding methods) have shown promising results, a

136 indicate that, compared to state-of-the-art graph embedding methods, hierarchical variational graph

137 We propose a specific knowledge graph embedding model, TriModel, to learn vector represe

138 h labeling method for vertex ordering in our graph embedding process.

139 trary to the common view, we argue that such graph embeddings do not capture salient properties of co

140 approach for representing tracking data as a graph, enabling individual tracking even in cases where

141 From raw grayscale data, we generated graphs encoding the 3-dimensional geometry of the left v

142 In models of excitable dynamics on graphs, excitations can travel in both directions of an

143 we develop a scalable implementation of the graph extension of the positional Burrows-Wheeler transf

144 the GEDFN architecture with a forest feature graph extractor, so that the feature graph can be learne

145 ent of long nucleotide sequences to assembly graphs, first general-purpose software tools for finding

146 d evidence provides a "gold-standard" causal graph for evaluation.

147 velop an algorithm for simplifying variation graphs for k-mer indexing without losing any k-mers in t

148 Balance Analysis and computes different flux graphs for visualization and analysis.

149 We construct multisample, colored de Bruijn graphs from short-read data for all samples, align long-

150 ree-dimensional atomic models from candidate graphs generated by RAG Sampler, and (c) RAG Designer: d

151 ent for real sequencing reads to a linear or graph genome.

152 ng epigenomic datasets with personalized and graph genomes allows the recovery of new peaks enriched

153 degree, position, and neighboring nodes in a graph has been proved to be informative in PPI predictio

154 While deep learning on graphs has dramatically advanced the prediction prowess,

155 in high-throughput screening data, scaffold graphs have proven useful for the navigation and analysi

156 an exponential growth law similar to random graphs if the number of nodes [Formula: see text] is bel

157 erical simulation for disease propagation in graphs imposing a local structure to allowed disease tra

158 We model the problem as a semi-bipartite graph in which we are able to use drug-drug and protein-

159 data and a spectrum of parameter values, as graphed in the P value function.

160 th global and regional disturbances to brain graphs in a group of healthy participants across baselin

161 showed some differences, but rhinomanometric graphs in vitro were close to the graphs in vivo.

162 manometric graphs in vitro were close to the graphs in vivo.

163 They can view the signal intensity graph including the binder/non-binder threshold followed

164 tistical methods for ancestral recombination graph inference and machine-learning methods for the pre

165 ing sequence composition, coverage, assembly graph information and network partitioning based on a pr

166 Facing the challenge of representing graph information, we introduce an improved graph repres

167 models for optimal partitioning of a signed graph into cohesive groups.

168 et, then conceptually merges those de Bruijn graphs into a single global one.

169 Although each path in the graph is a potential haplotype, most paths are non-biolo

170 Then a bipartite graph is constructed from the collected CSC genes along

171 The bipartite graph is then transformed into weighted bipartite graph

172 esents the first successful demonstration of graph kernels to protein interfaces for effective discri

173 We have then used a graph labeling method for vertex ordering in our graph e

174 linear equations that arise from a discrete graph Laplacian.

175 nterface (GUI)-based software tool that uses graph layout analysis with sequential time ordering to v

176 , we introduce computational models of human graph learning that make testable predictions about the

177 we highlight open questions in the study of graph learning that will require creative insights from

178 Here, we introduce graph learning, a growing and interdisciplinary field st

179 w evidence suggesting that both map-like and graph-like representations exist in the mind/brain that

180 ex information, cognitive maps and cognitive graphs may provide fundamental organizing schemata that

181 Further exploration of graph measures in the determined hub regions showed that

182 These graph measures were normalized by values obtained in equ

183 S patients and controls in global normalized graph measures, hubs, or modularity structure of the pai

184 n each subject to determine subject specific graph measures.

185 lues reaching the nanomolar level, with mean graph midpoints of 0.08-0.41 muM.

186 of such similarity networks, enabling visual graph mining techniques to uncover new insights from the

187 ents visGReMLIN, a web server that couples a graph mining-based strategy to detect motifs at the prot

188 te and investigate the weighted multifractal graph model corresponding to two real-world complex syst

189 covered models show that the proposed random graph model provides a novel way to understand the self-

190 tworks, we propose the weighted multifractal graph model to characterize the spatiotemporal complexit

191 Similarly, exponential random graph modelling of the social networks provided no evide

192 of each node alone, or equivalently, random graph models that preserve the degree of each node (i.e.

193 e network structures with exponential random graph models, and to mimic the data generation mechanism

194 shows that k-gram statistics with visibility graph motifs produce fast and accurate classifications,

195 Through graph network analysis, we found that calcium dynamics i

196 and tissues based on calcium fluorometry and graph network analysis.

197 lly advanced the prediction prowess, current graph neural network (GNN) methods are mainly optimized

198 Here, we present SkipGNN, a graph neural network approach for the prediction of mole

199 nformation by constructing a multirelational graph of drug-protein, disease-protein and PPIs.

200 REINDEER constructs the compacted de Bruijn graph of each dataset, then conceptually merges those de

201 edical knowledge bases to create a knowledge graph of entities connected to both drugs and their pote

202 be formulated as a link prediction task on a graph of interacting genes.

203 that the emergence of an interconnected sub-graph of stock returns co-movements from a broader marke

204 how how network measures applied to response graphs of large-scale games enable the creation of a lan

205 tope abundance, generate publication quality graphs of metabolite labeling, and present data in the c

206 Bayes method, and then building a bipartite graph on the generated modules using sparse regression,

207 ssuming the same node attributes in a global graph, our approach models interdomain information fusio

208 eural network to predict cell boundaries and graph partitioning to segment cells based on the neural

209 analogue solutions to a computationally hard graph-partitioning problem.

210 ctivity information, and call variants using graph path-finding algorithms and a model for simultaneo

211 We show that using a graph personalized genome represents a reasonable compro

212 sponding to tools developed using our RNA-As-Graphs (RAG) approach.

213 The topology of the network graph reflects established biology, with samples from re

214 ver, due to the greater complexity of genome graphs relative to linear genomes, some functions that a

215 nal graph auto-encoder (S-VGAE), an improved graph representation learning method, to automatically l

216 graph information, we introduce an improved graph representation learning method.

217 K energy terms with a score obtained using a graph representation of the protein-protein interfaces a

218 Graph representations of genomes are capable of expressi

219 The tool parses SBML and derives C-graph representations of the biological pathway with mas

220 ultiscale statistics from subsequent network graph representations.

221 We graphed results supine, 1-minute post-tilt-up, mid-tilt,

222 he problem as a link prediction in knowledge graphs (robust, machine-readable representations of netw

223 ning a reinforcement learning-based chemical graph-set designer.

224 ecast the drug-combination design problem as graph-set generation and developed a deep learning-based

225 Calibration graphs showed good linearity with coefficients of determ

226 uding categorical data, continuous data, bar graphs, side bar graphs and data that describes relation

227 of cloud points into stem and lamina points, graph skeletonization of the stem points, segmentation o

228 Here, we evaluate the use of variation graph software vg to avoid reference bias for aDNA and c

229 ing method, to automatically learn to encode graph structure into low-dimensional embeddings.

230 ed SV and CNV data into a unifying karyotype-graph structure to present a more accurate representatio

231 rug combination design, by jointly embedding graph-structured domain knowledge and iteratively traini

232 ecular profiles of patients are modeled in a graph-structured space that represents gene expression r

233 tracts multiple overlapping directed acyclic graph structures from a raw sentence.

234 et is more challenging to compute in sparser graphs than in denser ones.

235 n spatial regions and clusters and a cluster graph that shows the relationships between clusters at d

236 ausal relationships with the "gold standard" graph that was constructed from literature.

237 amples of transient amplifiers of selection (graphs that amplify selection for a particular range of

238 ifying viral genomes in metagenomic assembly graphs that is based on analyzing variations in the cove

239 Dedicated CSD methods managed to discover graphs that nearly coincided with the gold standard.

240 Graph theoretic analyses of 7.0-Tesla resting-state fMRI

241 rformance in patients can be predicted using graph theoretic measures from each subnetwork.

242 We examine how graph theoretic measures within these subnetworks relate

243 me warping, while relying on high throughput graph-theoretic algorithms for efficient exploration of

244 s including visualisation and clustering and graph-theoretic analysis of neuronal branching.

245 sional street connectivity measures based on graph-theoretic and geographic concepts.

246 s with millions of cloud points, and several graph-theoretic and machine-learning algorithms for 3D a

247 tivity using traditional measures as well as graph-theoretic measures of centrality.

248 fy the tightness of knowledge networks using graph theoretical indices and use a generative model of

249 We propose a novel graph-theoretical framework, the Corrected Gene Proximit

250 We developed a novel, to our knowledge, graph-theoretical method to distinguish genuine CFC from

251 ociation cortex and correlated with multiple graph-theoretical metrics of high functional connectedne

252 underlying idea of GOxploreR is to exploit a graph-theoretical perspective of GO as manifested by its

253 As informed by chemical graph theory (CGT), the vast majority of species observe

254 In graph theory analyses of these networks, functional conn

255 Graph theory analysis revealed differences in several gl

256 Graph theory analysis shows that the lamin meshwork is n

257 Additionally, no studies have utilized graph theory analysis to clarify whether sleep-related d

258 We also used exploratory graph theory analysis to compare topological properties

259 Rats were imaged at 11.1 T and graph theory analysis was applied to matrices generated

260 p between the SRI and DMN was examined using graph theory analysis.

261 hase portrait analysis as a mediator between graph theory and deep learning approaches.

262 tion search and localizes O-glycosites using graph theory and probability-based localization.

263 roimaging-gene expression study, we used two graph theory approaches to elucidate ELM subtype effects

264 Graph theory approaches to understanding decision-making

265 We computed network properties using graph theory from probabilistic tractography and calcula

266 We applied graph theory measures of global and local efficiency to

267 MVPA was not related to DMC or graph theory measures.

268 Graph theory metrics probed sex hormones' influence on t

269 bWPLI), for both whole-head connectivity and graph theory metrics.

270 Applications of machine learning and graph theory techniques to neuroscience have witnessed a

271 r method combines computational geometry and graph theory to measure the degree of order of any packe

272 lterations from a network perspective, using graph theory, and examined whether injury severity affec

273 f protein disorder, switchable interactions, graph theory, and multiple interacting dense phases.

274 A graph theory-based analysis of the brain-wide water flow

275 etries of residues are characterized here by graph theory.

276 ly quantifies its topological property using graph theory.

277 al DFT techniques and the recently developed Graph Theory.

278 ral network models based on tissue volume or graph-theory measures from whole-brain diffusion tensor

279 sed transductive algorithm is applied to the graph to explore the overall topology of the graph and t

280 s that exploits the sparsity of DNA assembly graphs to efficiently extract subgraphs surrounding an i

281 Approaches based on compacted de Bruijn graphs to identify and extend anchors into locally colli

282 ground heterogeneity on properly two-colored graphs to those with alternative schemes in which the co

283 age includes an implementation of visibility graphs to visualize time series as networks.

284 The variation graph toolkit (VG) represents genetic variation as a gra

285 three modules: (a) RAG Sampler: samples tree graph topologies from an RNA secondary structure to pred

286 old onto novel RNA motifs (described by tree graph topologies).

287 ariant (SNV) co-occurrence matrix and greedy graph traversal algorithm.

288 g a graph-based algorithm (topological tumor graphs, TTG).

289 ork analysis of fMRI data based on the multi-graph unsupervised Gaussian embedding method (MG2G).

290 d as a spanning tree, dendrogram or complete graph using different layouts.

291 t 8.4 x 10(5) NPTs L(-1) and the calibration graph was linear up to 3.5 x 10(8) NPTs L(-1).

292 Single-subject gray matter graphs were extracted from structural MRI scans, and who

293 A GSP is a graph where the nodes represent entities of the protein-

294 tivity, we summarized activity as functional graphs where edges between units are defined by pairwise

295 anisations modelled as hierarchical directed graphs, where the directed edges indicate influence.

296 eplace the linear reference with a variation graph which includes known alternative variants at each

297 They form connected graphs whose topography differs substantially among phen

298 nularity as possible, e.g., by replacing bar graphs with scatter plots wherever feasible and violin o

299 , we employ a novel node kernel suitable for graphs with typed edges.

300 deled by representing spatial structure as a graph, with individuals occupying vertices.