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1 oring multibacteria relationships (microbial cliques).
2 (i.e., who belongs to which communities and cliques).
3 enic cooperation with all other genes in the clique.
4 promoters known as enhancer-promoter hubs or cliques.
5 ly fixed and smaller neighborhood systems or cliques.
6 number of cells that participate in multiple cliques.
7 sks that we compile a listing of all maximal cliques.
8 ce of a network core and the distribution of cliques, (2) global and local binary properties, (3) glo
10 rm a global alignment by employing a maximum clique algorithm on a specially defined graph that we ca
11 romoter-enhancer interaction motifs, namely, cliques and networks, and interactions that are dependen
12 h asks us to determine the size of a largest clique, and the maximal clique enumeration problem, whic
13 hies, friendship networks are organized into cliques, and comparative relations (e.g., "bigger than"
15 sional spaces, rings, dominance hierarchies, cliques, and other forms and successfully discovers the
16 e peak matches in a graph, finds the maximal cliques, and then combines cliques with shared peaks to
18 addition, activation patterns of the neural clique assemblies can be converted to strings of binary
19 s of the DMN were identified, as well as the cliques associated with a reduced preference for motor p
20 thm achieved a considerable improvement over clique based algorithms in terms of its ability to recov
23 l approaches, such as mean shortest path and clique-based metrics, have been useful but are not well
24 reased odds of having the two-taxa microbial clique below the median relative abundance (odds ratio (
25 lustering, to graphical approaches such as k-clique communities, weighted gene co-expression networks
26 hat the problems remains hard in the case of clique complexes, a family of simplicial complexes speci
27 elative packing group (RPG) that applies the clique concept from graph theory as a natural basis for
28 ree topologies is computed using the maximal cliques concept, in phase II divergence times for each o
30 a graph clustering problem called the quasi-clique decomposition problem, which has recently also be
31 ormalize the commonness of common sense as a clique detection problem on a bipartite belief graph of
32 MATE-CLEVER (Mendelian-inheritance-AtTEntive CLique-Enumerating Variant findER) as an approach that a
34 he size of a largest clique, and the maximal clique enumeration problem, which asks that we compile a
38 elation list can be examined using the novel clique finder tool to determine the sets of genes most l
41 rk analysis, including identifying defective cliques, finding small network motifs (such as feed-forw
42 novel method that employs heaviest weighted clique-finding (HCF), which we show significantly outper
44 We developed a new computational method, RNA-clique, for calculating genetic distances using assemble
47 with an alternative method revealed that RNA-clique has relatively high time and memory requirements,
50 framework for motif finding through finding cliques in a graph but have made this framework substant
51 CliXO is a new approach that finds maximal cliques in a network induced by progressive thresholding
52 independent and recurrent SCNA: s as maximal cliques in an interval graph constructed from overlaps b
54 ; (2) social cohesion, such as the extent of cliques in friendship networks; and (3) civic engagement
56 of network-level coding units, termed neural cliques, in the hippocampus has enabled real-time patter
57 onary game theory model, that cooperation on cliques increases linearly with community motif count.
58 milarly to the case of single-node or single-clique initiators studied previously, we observe that co
59 population activity of such pyramidal neuron clique is temporally linked to the activity of the local
60 Many traditional graph algorithms such as k-clique, k-coloring, and subgraph matching have great pot
61 ce of algorithms on inherently low-diameter, clique-like benchmarks may not always be indicative of e
63 l scales, one can detect both clique- or non clique-like communities without imposing an upper scale
64 ume an implicit notion of community based on clique-like subgraphs, a form of community structure tha
65 m geometric constraints can have natural non clique-like substructures with large effective diameters
66 cer and promoter spatial clusters termed "3D cliques." Loss- and gain-of-function experiments show th
68 tuned interneurons and pyramidal cells into cliques may ensure that ensembles of functionally alike
70 thm (QCM algorithm) into edge-covering Quasi-Clique Merger algorithm (eQCM) for mining weighted sub-n
72 -expression networks and local maximal quasi-clique merger to identify gene co-expression modules.
74 s for a set of chromatograms, the Consistent Cliques Method (CCM) represents all peaks from all chrom
75 he protein interaction network for defective cliques (nearly complete complexes of pairwise interacti
76 ically alter the structure of scale-free and clique networks and show, through a stochastic evolution
78 ls and other cell types have revealed that a clique of self-regulated core TFs control cell identity
79 g Monte Carlo simulations, we identified two cliques of co-expressed genes that were significantly en
81 twork architecture characterized by neuronal cliques of dense local connectivity communicating with e
82 sses small-world network architecture, where cliques of densely interconnected neurons ("small worlds
84 own and widely-studied problems: the maximum clique optimization problem, which asks us to determine
85 ughput network data has been to extract near-cliques or highly dense subgraphs from a single protein-
86 e network at all scales, one can detect both clique- or non clique-like communities without imposing
90 a novel in-house algorithm and a tailor-made Clique Percolation Method to extract linear and nonlinea
92 contribution is the careful selection of the clique potential functions in the MRF so its maximum a p
93 that: (1) in mouse V1 individual small-world cliques preferably incorporate pyramidal neurons with si
94 by devising a scoring function, the Maximum Clique problem being a classic example, where S includes
95 gorithm was implemented to solve the maximal clique problem for a simple graph with six vertices.
97 nally, we show that solving a single Maximum Clique problem using parallel quantum annealing reduces
98 metric for solving instances of the Maximum Clique problem when compared to solving each problem seq
100 Although based on solutions to the maximum clique problem, this algorithm deals properly with ambig
103 find an optimal selection of non-overlapping cliques, resulting in a very fast algorithm, which we ca
104 ements, the comparisons also showed that RNA-clique's results were at least as reliable as the altern
106 eraction networks were comprised of numerous cliques-sets of three or four genes such that each TSG w
107 or four genes such that each TSG within the clique showed oncogenic cooperation with all other genes
108 dissolution of intricate TAD-like structure cliques showing long-range interactions represents a pro
109 ature has classified these groups as support cliques, sympathy groups, bands, cognitive groups, tribe
111 exposure, we identified a two-taxa microbial clique that included Bifidobacterium adolescentis and Ru
113 tch preferentially targets hyperconnected 3D cliques that regulate the expression of crucial proto-on
114 -based inference to first identify microbial cliques that were predictive of prenatal Pb exposure and
115 describe groups of amino acid sites called "cliques" that were highly associated with each other.
116 ve cospiking" dynamics that allow the neural clique to overcome the response variability of its membe
118 a novel approach to matrix analysis, called clique topology, that extracts features of the data inva
119 sponding to the total ensemble of six-vertex cliques was built, followed by a series of selection pro
123 detection algorithm to assign plasmids into cliques, which correlate with plasmid gene content, bact
124 finds the maximal cliques, and then combines cliques with shared peaks to extract reliable features.
126 e reduced abundance of a probiotic microbial clique within the gut microbiome in late childhood.
127 el analytical approach to identify microbial cliques within the gut microbiome of children at 9-11 ye
129 ied, and behavioral domain analysis of these cliques yielded discrete functional properties, demonstr