ライフサイエンスコーパス: graph

1 mal clique finding problem in a multipartite graph.

2 function heatmap and a BC similarity network graph.

3 topology was also demonstrated in the binary graph.

4 ces rely on in-memory representation of this graph.

5 ng local mixing properties of the underlying graph.

6 l architecture when applied to the atom-rule graph.

7 n in complexes and sets, is addressed with a graph.

8 ks and the Kirchhoff index of the underlying graph.

9 m RNA-seq reads based on the model of splice graph.

10 d to facilitate fast mapping of reads to the graph.

11 ncreased topological integration of a binary graph.

12 eating an interactive alternative to the bar graph.

13 tion the newly generated, attribute-enhanced graph.

14 VO2) to efficiently solve vertex coloring of graphs.

15 ty of biological data onto molecular pathway graphs.

16 for feature learning on biological knowledge graphs.

17 ng in networks that are often represented by graphs.

18 ode for related information within knowledge graphs.

19 amples graph topologies to produce candidate graphs.

20 xplanation based on evolutionary dynamics on graphs.

21 e implicit encoding of discrete, non-spatial graphs.

22 works belong to a family of planar proximity graphs.

23 -read error-correction methods use de Bruijn Graphs.

24 be guided using tools such as direct acyclic graphs.

25 ndividual data points may be overlaid on the graphs.

26 spacetimes are as good as random hyperbolic graphs.

27 ulation structures represented as undirected graphs.

28 Comet and Comet-swarm families of undirected graphs.

29 udy looks at directed and hence more general graphs.

30 l systems to approximate optimal coloring of graphs.

31 he subsequent comparison of the structure of graphs.

32 qbal et al. introduced the colored de Bruijn graph, a variant of the classic de Bruijn graph, which i

33 roblem (the removal of vertices, leaving the graph acyclic).

34 epresentation of proteoforms and design mass graph alignment algorithms.

35 Using the Google Trend 5-year graph, an annual and clear seasonality of queries was fo

36 Graph analyses revealed a shift of the regional architec

37 sor imaging tractography, and analysed using graph analysis and network-based statistics to explore b

38 mplified by a single primer pair, a directed graph analysis method is used to identify minimum amplic

39 network topology was characterized by three graph analysis methods including the commonly-used weigh

40 Our integrated knowledge graph, analytic tools, and web services enable diverse u

41 other specific protein within a KEGG pathway graph and (iii) propose a method for interpreting the cl

42 inst Google's manually-constructed knowledge graph and against expert physician opinions.

43 It then constructs a contig graph and, for each long read, references the other long

44 To alleviate this problem, we developed a graphing and analytical approach for use with more than

45 Traditional graphs and charts failed to convey the complex shapes of

46 the embedding for general complete bipartite graphs and logical disjunctions may be of broader use th

47 Results in both synthetic random graphs and real networks show that the proposed method c

48 rediction) represents RNA structures as tree graphs and samples graph topologies to produce candidate

49 square and triangular lattices, a disordered graph, and demonstrate the potential for size scalabilit

50 ly expensive, requires less information on a graph, and is free from nuisance parameters.

51 data distributions can lead to the same bar graph, and the actual data may suggest different conclus

52 strongest amplifier known has been the Star graph, and the existence of undirected graphs with stron

53 ent degrees, from the circle to the complete graph, and vary assumptions on the fitness probability d

54 d can be applied to any biological knowledge graph, and will thereby open up the increasing amount of

55 We define colored-weighted graphs, and introduce different products between them to

56 ring the missing links in randomly generated graphs, and then generalize these tasks to specific grap

57 ly module parses aligned reads into splicing graphs, and uses network flow algorithms to select the m

58 sults extend beyond the grid to more general graphs, and we discuss applications to size estimation f

59 res-which we collectively refer to as genome graphs-and discuss the improvements in read mapping, var

60 The statistical properties of this graph are based on distributions gathered from three-dim

61 network measures applied to the induced flat graphs are accurate predictors of network propagation in

62 Simple models of excitable dynamics on graphs are an efficient framework for studying the inter

63 ability, and it remains unclear if de Sitter graphs are as navigable as hyperbolic ones.

64 Although bar graphs are designed for categorical data, they are routi

65 ing, and haplotype determination that genome graphs are expected to produce.

66 We find these graphs are navigable only in the manifolds with dark ene

67 easure returns non-zero values only when the graphs are non-isomorphic.

68 t), a 2D lattice and a mass-action (complete graph) arrangement.

69 th more restricted assumptions map onto this graph as special cases.

70 ng number of metabarcoding studies, is often graphed as stacked bar charts or pie graphs that use col

71 e and investigate the potential of polariton graphs as an efficient analogue simulator for finding th

72 luding the commonly-used weighted and binary graph, as well as minimum spanning tree (MST).

73 repeat-resolution capabilities of de Bruijn graph assemblers.

74 Successively, horizontal visibility graphs associated with all neurons become layers of a la

75 and confidence interval for each edge in the graph, based on a recent proposal by Ren et al., 2015.

76 We propose a new approach that uses a graph-based algorithm with a two-phase sampling method t

77 The performance of graph-based algorithms has the advantage of being faster

78 n time, but they tend to perform better than graph-based algorithms.

79 Altered coupling was explained using a graph-based analysis of experimentally established struc

80 We used a high-resolution graph-based approach to investigate local-to-local and l

81 annot be linearly represented, Canu provides graph-based assembly outputs in graphical fragment assem

82 m different technologies through a bipartite-graph-based clustering algorithm, our approach turns a w

83 methods for fiber bundle reconstruction and graph-based connectivity analysis.

84 Here we present our graph-based fragment assembly algorithm (F-RAG) to conve

85 In this work, we adopt a graph-based framework to interpret and characterize inte

86 Here, we propose Gracob, a novel, efficient graph-based method that casts and solves the constant-co

87 The method, named Graph-based Model Quality assessment method (GMQ), expli

88 ideas implicitly or explicitly borrowed from graph-based models.

89 different integration algorithms, including graph-based semi-supervised learning, graph sharpening i

90 ormation of nucleic acid properties into our graph-based signatures, considering the reverse mutation

91 We developed TopMG, a mass graph-based software tool for proteoform identification

92 s projects underway to build and apply these graph-based structures-which we collectively refer to as

93 st and applicable in most weighted de Bruijn Graph-based systems.

94 wo common classes of integration algorithms, graph-based that depict relationships with subjects deno

95 ere clears the way to the application of the graph-based theory to single-molecule data for the descr

96 ulation level data, it is essential that the graphs be represented efficiently.

97 Vertex coloring of graphs, belonging to the class of combinatorial optimiza

98 We first construct a Bi-relational graph (Birg) model comprised of both protein-protein ass

99 e to the global topology of a weighted brain graph, but incrementally increased topological integrati

100 des solving optimization problems, polariton graphs can simulate a large variety of systems undergoin

101 rom a community of interest in an underlying graph, can we reliably identify the rest of the communit

102 Chain event graphs (CEGs) are a graphical representation of a statis

103 Here, we present a proximity-based graph clustering approach to identify TF clusters using

104 ul new connections between phylogenetics and graph clustering.

105 ynamical systems and spectral algorithms for graph coloring.

106 numerous methods for representing de Bruijn Graphs compactly.

107 f-idf weighted MinHash and a sparse assembly graph construction that avoids collapsing diverged repea

108 We constructed a knowledge graph containing four types of node: drugs, protein targ

109 The compressed graphs convey network motifs and architectural features

110 rating that the TF-TF connections within the graph correlate with biological TF-TF interactions.

111 Calibration graphs could not be obtained on unpolished electrodes in

112 erebellar lobule volumes were derived from a graph-cut based segmentation algorithm.

113 features have been developed using the Neo4j graph database technology and this paper describes key f

114 The Fragment Network is a graph database that allows a user to efficiently search

115 variants and well phenotyped patients into a graph database to allow fast efficient storage and query

116 l data which can be queried using a flexible graph database.

117 will create a comparative, multi-dimensional graph database.

118 E consists of three integrated components: a graph-distance-based heat map normalization tool, a 3D m

119 ative data structures to encode the assembly graph efficiently in a computer memory.

120 distributions of quantum walks on circulant graphs efficiently.

121 e between regions of highest importance from graph eigenmodes of network diffusion and nexus regions

122 haplotype model that operates on a variation graph-embedded population reference cohort.

123 t Monocle 2, an algorithm that uses reversed graph embedding to describe multiple fate decisions in a

124 We employ a flexible graph encoding to preserve multiple structural hypothese

125 Using the Electrical Penetration Graph (EPG) technique, we found that mycorrhizal colonis

126 Persistent homology with graph filtration on alpha-gamma correlation disclosed to

127 Our graph filtration over MEG network revealed these inter-r

128 We demonstrate that it can construct the graph for 100 simulated human genomes in less than a day

129 ic Web technologies and the use of knowledge graphs for data integration, retrieval and federated que

130 solution of simply plotting two-dimensional graphs for every combination of observables becomes impr

131 ropose a new data structure, called the mass graph, for efficient representation of proteoforms and d

132 er than the fixation probability of the Star graph, for fixed population size and at the limit of lar

133 Weighted graph found significantly decreased path length together

134 rect construction of the compacted de Bruijn graph from a set of complete genomes.

135 xes we learn a sparse conditional dependency graph from approximately 3,000 CF-MS experiments on huma

136 onstruction of high quality health knowledge graphs from medical records using rudimentary concept ex

137 In addition, graph-GPA also promotes better understanding of genetic

138 f GWAS datasets for 12 phenotypes shows that graph-GPA improves statistical power to identify risk va

139 Application of graph-GPA to a joint analysis of GWAS datasets for 12 ph

140 r, we propose a novel statistical framework, graph-GPA, to integrate a large number of GWAS datasets

141 The graph-guided methods overcome this drawback by using the

142 tomic embedding of the edges of the cerebral graph have been postulated to elucidate the relative imp

143 de Bruijn graphs have been proposed as a data structure to facilit

144 es of the mutants, the Comet and Comet-swarm graphs have fixation probability strictly larger than th

145 ompactly representing the weighted de Bruijn Graph (i.e. with abundance information) with essentially

146 The visualization graphs illustrate discriminated biobricks and inappropri

147 the optimum condition, a linear calibration graph in the range of 4.00-100.00mgL(-1) was obtained wi

148 , in terms of navigability, random geometric graphs in asymptotically de Sitter spacetimes are as goo

149 In that respect, random geometric graphs in asymptotically de Sitter spacetimes, such as t

150 Random geometric graphs in hyperbolic spaces explain many common structur

151 widespread use of sparse residue interaction graphs in protein design, the above mentioned effects of

152 e study the navigability of random geometric graphs in three Lorentzian manifolds corresponding to un

153 se our related RAG-3D utilities to partition graphs into subgraphs and search for structurally simila

154 A variation graph is a mathematical structure that can encode arbitr

155 es faster than Isomap, and its visualization graph is more concentrated to discriminate biobricks.

156 0 amino acids (proteins), and the mutational graph is not the hypercube.

157 ifying and quantifying dissimilarities among graphs is a fundamental and challenging problem of pract

158 Yet another important property of hyperbolic graphs is their navigability, and it remains unclear if

159 anning branching structures in the de Bruijn graph) is used to facilitate fast mapping of reads to th

160 g, and designing RNA structures, RAG (RNA-As-Graphs), is presented, with the goal of understanding fe

161 by a tree but is instead a directed acyclic graph known as a phylogenetic network.

162 a composite measure of grey matter volume by graph-Laplacian principal component analysis, and then f

163 obial Prior Lasso (MPLasso) which integrates graph learning algorithm with microbial co-occurrences a

164 as used to automatically construct knowledge graphs: logistic regression, naive Bayes classifier and

165 modern life depend upon colour coding (e.g. graphs, maps, signals), the impact of colour blindness o

166 Here, we present Methyl Assignment by Graph Matching (MAGMA), for the automatic assignment of

167 A graph matching protocol examines all possibilities for e

168 employing steerable filtering and iterative graph matching to characterize the fibers embedded in th

169 Using a simple graph metric, global network changes are observed over t

170 ations of 27 published models using standard graph metrics.

171 tween groups and also compared the groups on graph metrics.

172 n of the new compounds had cytotoxicity mean-graph midpoint (MGM) GI50 values in the submicromolar (0

173 e correlation Generalized Exponential Random Graph Model (cGERGM) - a statistical network model that

174 ank to diffuse information on this two-layer graph model.

175 Five graph models were fit using data from 1574 people who in

176 onvert candidate three-dimensional (3D) tree graph models, produced by RAGTOP into atomic structures.

177 Simulating graph models, we identify a heuristic cellular division

178 ix and to decompose correlations in terms of graph motifs.

179 simple invariant that all weighted de Bruijn Graphs must satisfy, and hence is likely to be of genera

180 Vessel images were transformed to graph networks and segmented to branches to reduce the c

181 Weighted graph networks were then converted to circuits, in which

182 Many de novo assemblers using the de Bruijn graph of a set of the RNA sequences rely on in-memory re

183 A graph of disease-symptom relationships was elicited from

184 ovides an interactive and integrated network graph of gene and iTerms that allows filtering, sorting,

185 lex systems can be predicted solely from the graph of interactions between variables, without conside

186 s approach adds the k Nearest Neighbor (kNN) graph of node attributes to alleviate the sparsity and t

187 y building a representation of the de Bruijn Graph of the reads they are given as input.

188 ing fMRI data, where networks are defined as graphs of interacting brain areas.

189 or binding sites enriched in superenhancers, graphs of the metrics evaluating the superenhancers qual

190 based on the Prize-collecting Steiner Forest graph optimization approach.

191 ution for weak selection that applies to any graph or network.

192 g groups and the OSATS or OSANTS scores were graphed per group to determine the performance standard.

193 Our embedding on the Chimera graph preserves the structure of the original SCP instan

194 ment assembly algorithm, leveraging assembly graphs provided by a conventional de novo assembler and

195 ackage for easily parsing, manipulating, and graphing publication-ready plots of hierarchical data.

196 In the case of complete graphs, randomness accelerates mutant fixation for all p

197 opulation sizes, and in the case of circular graphs, randomness delays mutant fixation for N larger t

198 y OR model produces a high quality knowledge graph reaching precision of 0.85 for a recall of 0.6 in

199 e found that using different edge weights in graphs reflecting different secondary structures further

200 Ancestral recombination graphs represent potential histories that explicitly acc

201 18-28% compared to the approximate de Bruijn graph representation in Squeakr.

202 ty of spatially neighboring residues using a graph representation of a query protein structure model.

203 Unfortunately, current succinct de Bruijn graph representations are not directly applicable to the

204 tocol to construct and sample coarse-grained graph representations of RNAs from a given secondary str

205 to the prediction of edges in the knowledge graph representing problems of function prediction, find

206 Because of the centrality of de Bruijn Graphs, researchers have proposed numerous methods for r

207 ials to our RAGTOP (RNA-As-Graph-Topologies) graph sampling protocol to construct and sample coarse-g

208 We present graph-sampling results for 35 RNAs, including 12 k-turn

209 luding graph-based semi-supervised learning, graph sharpening integration, composite association netw

210 We then apply our graph structure beyond clustering, using it to increase

211 Our graph structure is able to significantly increase the ac

212 higher sensitivity and specificity when the graph structure is correctly specified, and are fairly r

213 archical classification setting, (i) use the graph structure of KEGG pathways to create a feature typ

214 ization frameworks but utilize the same data graph structure, which allows a highly efficient, expand

215 ture learning methods that are applicable to graph-structured data are becoming available, but have n

216 ified, and are fairly robust to misspecified graph structures.

217 and then generalize these tasks to specific graphs such as transport networks and family trees.

218 dy how small changes in population structure-graph surgery-affect evolutionary outcomes.

219 esults suggest the potential of the spectral graph techniques in characterizing and modeling river ne

220 ently displays each rule, (ii) the atom-rule graph that conveys regulatory interactions in the model

221 GE outputs a visually interpretable assembly graph that encodes all possible finished assemblies cons

222 s often graphed as stacked bar charts or pie graphs that use color to represent taxa.

223 In the regime of undirected graphs, the strongest amplifier known has been the Star

224 e to address network-level questions using a graph theoretic analysis of functional connectivity data

225 Here we construct a graph-theoretic algebra (a subset of Temperley-Lieb alge

226 By performing graph-theoretic analyses on thalamocortical functional c

227 Edge-centric graph-theoretic analysis showed that highly selected whi

228 f the sRNA-target networks in bacteria using graph-theoretic approaches and review the local integrat

229 eakly correlated) materials, consisting of a graph-theoretic description of momentum (reciprocal) spa

230 In this work, through a graph-theoretic formulation of drainage river networks,

231 from environmental conditions is difficult, graph-theoretic properties of the mutational networks en

232 Whole-brain data-driven graph theoretical analysis disclosed that striatal regio

233 ng a neural community detection strategy and graph theoretical analysis of functional MRI data in hum

234 Herein we examined network changes using graph theoretical analysis of high-density EEG during pa

235 Graph theoretical analysis of the community structure of

236 We applied graph theoretical analysis to task-related fMRI function

237 (r = -0.51, p = -0.002) were shown, through graph theoretical analysis, to be significantly negative

238 twork architecture and global topology using graph theoretical analysis.

239 tworks were reconstructed and examined using graph theoretical analysis.

240 d two model-based measures: 'integration', a graph theoretical measure obtained from functional conne

241 more, we demonstrate that extremal values of graph theoretical measures (e.g., degree and betweenness

242 METHOD: Graph theoretical methods were used to examine global an

243 sights into fundamental network alterations, graph theoretical models of brain networks were derived.

244 al changes in connectivity, we show that our graph-theoretical approach based on centrality (eigenvec

245 Network meta-analysis using a graph-theoretical approach was used to generate the indi

246 yzed with network-based statistics (NBS) and graph-theoretical methods.

247 Graph theory analysis showed that increased local effici

248 Graph theory analysis was applied to derive functional n

249 ning marine reserve networks that integrates graph theory and changes in larval connectivity due to p

250 process requires modeling of the brain using graph theory and the subsequent comparison of the struct

251 n the representation of connectomes by using graph theory formalisms.

252 First, we introduce a graph theory model for river networks and explore the pr

253 Here, we used graph theory to compare flexibility of network-level top

254 The current study applies concepts from graph theory to investigate the differences in lagged ph

255 agnetic resonance imaging data and performed graph theory to obtain network metrics of integration wi

256 tional connectivity of the left IFG and used graph theory to study its local functional network topol

257 connect concepts from lattice field theory, graph theory, and transition rate theory to understand h

258 Using graph theory, we visualize and quantify spectral connect

259 In this study, we applied two complementary graph theory-based functional connectivity analyses, one

260 tivity strength, a whole-brain, data-driven, graph theory-based method, was applied to resting-state

261 This technique employs a local-search graph-theory approach to discover novel motifs in patien

262 nce methods (in particular, directed acyclic graphs), they describe different threats to the validity

263 For a large class of random graphs, this problem is tightly connected to the decycli

264 Most assembly pipelines require this large graph to reside in memory to start their workflows, whic

265 umulating Evidence and Research Organization graphing to depict the portfolio of research activities

266 In this paper, we use directed acyclic graphs to characterize potential biases in studies of in

267 cy of GMQ was improved by considering larger graphs to include quality information of more surroundin

268 ssign read counts from the nodes of splicing graphs to transcripts.

269 ts RNA structures as tree graphs and samples graph topologies to produce candidate graphs.

270 ese scoring potentials to our RAGTOP (RNA-As-Graph-Topologies) graph sampling protocol to construct a

271 structure prediction protocol RAGTOP (RNA-As-Graphs Topology Prediction) represents RNA structures as

272 rk consisting of outrigger, a de novo splice graph transversal algorithm to detect AS; anchor, a Baye

273 These graph types do not convey the hierarchical structure of

274 otation score optimization, and (vi) network graph visualizations, data curation, and sharing are mad

275 The dynamic range of the calibration graph was 0.03-0.50mumolL(-1) CoQ10, and the detection l

276 nder the optimum conditions, the calibration graph was found to be LOQ-250microgL(-1) (r(2)=0.9987) f

277 er the optimized conditions, the calibration graph was linear in the range of 0.017-3.0mugL(-1), with

278 The calibration graph was linear in the range of 0.02-2.50microgL(-1), w

279 er the optimized conditions, the calibration graph was linear in the range of 0.3-300mugL(-1) (R(2)=9

280 g geodesics with minimum distance paths on a graph, we analyze the distribution of pairwise distances

281 Using this graph, we developed a machine learning algorithm based o

282 of designing with sparse residue interaction graphs, we computed the GMECs for 136 different protein

283 ned parameters and the constructed knowledge graphs were evaluated and validated, with permission, ag

284 Directed acyclic graphs were used to identify potential confounders, and

285 and proteins can be identified when assembly graphs were utilized, improving the characterization of

286 hinges" to reads for constructing an overlap graph where only unresolvable repeats are merged.

287 presented using a sparse residue interaction graph, where the number of interacting residue pairs is

288 jn graph, a variant of the classic de Bruijn graph, which is aimed at 'detecting and genotyping simpl

289 directly applicable to the colored de Bruijn graph, which requires additional information to be succi

290 ve fugacity on the Hamiltonian's interaction graph, which, as a statistical mechanics problem, is of

291 Markov Clustering Algorithm to partition the graph while maintaining TF-cluster and cluster-cluster i

292 p the benefits of sparse residue interaction graphs while avoiding their potential inaccuracies.

293 larities is provided by means of a bipartite graph, whose layout is heuristically optimized.

294 ron activations with a horizontal visibility graph, whose topological properties have been shown to b

295 tions are based on Boolean networks-directed graphs with 0/1 node states and logical node update rule

296 udy spatial arrangements of cells on regular graphs with different degrees, from the circle to the co

297 combination of such highly resolved assembly graphs with long-range scaffolding information promises

298 el way to combine sparse residue interaction graphs with provable, ensemble-based algorithms to reap

299 Star graph, and the existence of undirected graphs with stronger amplification properties has remain

300 model the cytoskeleton as a random geometric graph, with nodes corresponding to junctional complexes

301 uired to store and use the colored de Bruijn graph, with some penalty to runtime, allowing it to be a