ライフサイエンスコーパス: greedy

1 Using a propensity score and a greedy 5 to 1 digit-matching algorithm, 106 of these pat

2 A greedy algorithm allowing for partial overlaps was, thus

3 edy algorithm outperformed the Bidirectional Greedy algorithm and other methods, particularly in redu

4 utationally prohibitive, our approach uses a greedy algorithm based on windows of fixed sizes.

5 It uses a non-greedy algorithm but still maintains a speed comparable

6 We also show how the greedy algorithm can be used to solve some special cases

7 The authors show that a greedy algorithm does not find networks' maximum nestedn

8 We also design GreedyAlloc, a greedy algorithm for allocating the vaccine supply at th

9 Fourth, we describe a greedy algorithm for determining alignments of functiona

10 we further extend LCP(2) to a new algorithm (greedy algorithm for LCP(2)) GLCP(2) to identify overlap

11 ere more exhaustive searches can replace the greedy algorithm for tagSNP selection.

12 standalone software tool STRScan that uses a greedy algorithm for targeted STR profiling in next-gene

13 The quantum algorithm reduces to a classical greedy algorithm in the presence of strong noise.

14 10(5) links, while the applicability of the greedy algorithm is limited to individual pathways with

15 respectively, than those identified using a greedy algorithm of Patil et al.

16 The Greedy algorithm outperformed the Bidirectional Greedy a

17 In this article, we propose a novel semi-greedy algorithm over the space of all IUPAC degenerate

18 Intell.5, 24-25] point out that a simple greedy algorithm performs better than the GNN.

19 Further, we design a greedy algorithm solution for a fast solution.

20 We have developed a greedy algorithm that runs in polynomial time and guaran

21 For more general cases, we develop a greedy algorithm to approximate the minimum set of drive

22 GFam is a hybrid approach that uses a greedy algorithm to chain component domains from InterPr

23 or a set of initial clusters and then uses a greedy algorithm to cluster sequences.

24 rings in the probe and describe an efficient greedy algorithm to design mammalian whole genome tiling

25 We introduce a greedy algorithm to maximize this posterior that we call

26 structure of the cost function, we design a greedy algorithm to obtain near-optimal approximations i

27 We also implement a simple greedy algorithm to optimise the link order in favour of

28 FindGDPs is a program that uses a greedy algorithm to quickly identify a set of genome-dir

29 presentative sets are found through a simple greedy algorithm using the HSSP-value to establish seque

30 A greedy algorithm was used to improve the efficiency.

31 Within Evitar, we develop Greedy Algorithm with Redundancy and Similarity-weighted

32 ithm with Redundancy and Similarity-weighted Greedy Algorithm with Redundancy to enhance the performa

33 , we developed a hybrid approach combining a greedy algorithm with the Expectation-Maximization (EM)

34 tum annealer with classical post-processing (greedy algorithm).

35 y aspects such as learning using a layerwise greedy algorithm, combining feedback information from mu

36 r system and associated tools, including its greedy algorithm, configurable matching strategies, and

37 We have developed an efficient, greedy algorithm, SEEDY, that extracts biologically rele

38 question, we present an algorithm-the Not-So-Greedy algorithm-to construct a sparse read-overlap grap

39 and optima, then connected into ridges by a greedy algorithm.

40 decoder generates a polished sequence with a greedy algorithm.

41 d of the density measure in each step of the greedy algorithm.

42 -product and (ii) use heuristics to design a greedy algorithm.

43 has a polynomial-time solution based on the greedy algorithm.

44 ures related to PD fail to be optimized by a greedy algorithm.

45 o maximize PD can be efficiently solved by a greedy algorithm.

46 hilic motif pairs, which are identified by a greedy algorithm.

47 hierarchical levels, and the deterministic, "greedy" algorithm that sequentially cuts the links that

48 still not quite as fast as the best possible greedy algorithms accelerated on Graphics Processing Uni

49 selection methods currently in use, such as greedy algorithms and exhaustive search are unsuitable f

50 We consider a variety of simple, well-known greedy algorithms for this problem and show the effectiv

51 then joined using a combination of graph and greedy algorithms to identify specific structural varian

52 We present ALFRED-G, a greedy alignment-free distance estimator for phylogeneti

53 We introduced and validated two algorithms, Greedy and Bidirectional Greedy, using curated protein s

54 low physics and social traits that influence greedy and cooperative group behavior.

55 blem has been assumed to be amenable only to greedy and heuristic methods.

56 rithmic perspective we derive entropy-driven greedy and message-passing algorithms that focus their s

57 In particular, greedy and reluctant schemes tend to favor ISs of marked

58 futures is then backtested by training both greedy and stepwise lookahead random forests to predict

59 Existing greedy and stochastic algorithms are not guaranteed to f

60 t MMFPh finds important motifs missed by the greedy approach of Motif-X, while also finding more moti

61 Although GRAPPA-TP uses a greedy approach to compute the transposition distance, i

62 c to compare profiles of DNA sequences and a greedy approach to search for common subprofiles.

63 Then a greedy approach was used to select the features that wer

64 compare the relative performance of a novel greedy approach with several other heuristic solutions.

65 In contrast to the greedy approach, the decision trees included in this ran

66 p to 50 times faster than the currently best greedy approach.

67 he alignment of multiple networks based on a greedy approach.

68 Compared with greedy approaches, BASS rapidly learns effective SPs for

69 ractable problem, we devise a parameter-free greedy approximation algorithm, termed Protein Complexes

70 We find that a greedy articulation point removal process provides us a

71 The algorithm starts from a greedy assignment and improves it through a constrained

72 several conjectures related to this quantum greedy basis and the triangular basis of Berenstein and

73 We identify a quantum lift of the greedy basis for rank 2 coefficient-free cluster algebra

74 Principal component analysis (PCA), Greedy, Best first, Genetic search, Random search as fea

75 ents are fit to the native structure using a greedy build-up method.

76 nlike most other assembly algorithms, Not-So-Greedy comes with a performance guarantee: whenever info

77 hia coli K12 dataset demonstrate that Not-So-Greedy compares favorably with standard string graph app

78 multi-cluster intermediates occurs through a greedy competition between clusters to recruit and retai

79 While a greedy consensus algorithm, which consecutively accepts

80 matrix representation with parsimony and the greedy consensus.

81 thm systematically outperforms its classical greedy counterpart, signaling a quantum enhancement.

82 Nonetheless, even the greedy CSP tended to be less accurate on our simulated t

83 ontinuities, we developed a new version, the greedy CSP, that shows reduced bias and improved accurac

84 what circumstances an implementation of less greedy decision trees actually yields outperformance.

85 nventionally, random forests are built from "greedy" decision trees which each consider only one spli

86 We evaluated two mapping strategies, greedy discard and varimax rotation, by assessing the ab

87 We also derive the Greedy Energy-Aware Control (GEAC) and Predictive Energy

88 ethods, Fast Causal Inference (FCI) and Fast Greedy Equivalence Search (FGES) in their ability to dis

89 rmined using the Independent Multiple sample Greedy Equivalence Search (IMaGES) and Linear non-Gaussi

90 lgorithms Necessary Path Condition (NPC) and Greedy Equivalent Search (GES).

91 escribe the HEDGES (Hash Encoded, Decoded by Greedy Exhaustive Search) error-correcting code that rep

92 'signatures', are iteratively clustered in a greedy fashion, retaining at each step the reference gen

93 s these shortcomings, we propose an adaptive greedy filtering algorithm based on a discretized mark p

94 otide variant (SNV) co-occurrence matrix and greedy graph traversal algorithm.

95 e described pairwise SNV matrix (Hansel) and greedy haplotype path traversal algorithm (Gretel) are o

96 e described pairwise SNV matrix (Hansel) and greedy haplotype path traversal algorithm (Gretel) is op

97 nt KwARG, which implements a parsimony-based greedy heuristic algorithm for finding plausible genealo

98 Our results suggest that our greedy heuristic algorithm not only works well but also

99 rection scores for all chromosomes) than the greedy heuristic and a previously published method, Fast

100 otypes estimated using a previously proposed greedy heuristic and a simple MCMC method.

101 tion problem, SVCollector implements a fast, greedy heuristic and an exact algorithm using integer li

102 near time and space complexity comparable to greedy heuristic clustering algorithms, while achieving

103 lete genome of a human individual and used a greedy heuristic to assemble the haplotypes for this ind

104 Areas selected by a complementarity-based greedy heuristic using our full ROI approach provided gr

105 algorithm, RNAtabupath, employs a tabu semi-greedy heuristic, known to be an effective search strate

106 cestral network reconstruction primarily use greedy heuristics and yield sub-optimal solutions.

107 , the length of the input sequence, and thus greedy heuristics are applied to speed up the extension.

108 compared to previously published linear-time greedy heuristics.

109 We also tested a greedy hill climbing algorithm and observed similar resu

110 ly requiring that these processing units be "greedy," i.e., not idle if they can perform a production

111 The sub-optimality of greedy implementation has been well-known, yet mainstrea

112 Most existing algorithms are based upon greedy isotope template matching and thus may be prone t

113 oyd's K-means Clustering and the Progressive Greedy K-means Clustering.

114 on algorithms, namely, Combo, Conclude, Fast Greedy, Leading Eigen, Louvain and Spinglass, on two imp

115 ices, but then combine partial searches with greedy local steps to solve subtasks, and maladaptively

116 er, if individuals are too conformist or too greedy, markers fail to shape social interactions.

117 t in the VATS L-MLND group using a 5:1 digit greedy match algorithm.

118 d using a multivariable logistic model and a greedy matching algorithm with a 1:1 ratio.

119 apping subgroups, using propensity score and greedy matching algorithms.

120 tched on their propensity scores using a 1:1 greedy matching technique.

121 pensity score matching (1:1 nearest-neighbor greedy matching), a risk analysis to investigate risk di

122 similarity metrics such as skip thought cs, greedy matching, vector extrema, and RAG answer similari

123 The greedy method was applied to allow treatment-control ran

124 Bipad is a C++ program that applies greedy methods to search the bipartite alignment space a

125 speed comparable to implementations based on greedy methods.

126 oids the most serious pitfalls caused by the greedy nature of this algorithm.

127 t treated with fluconazole and performed 1:1 greedy nearest neighbor propensity score matching to con

128 kahead version significantly outperforms the greedy one when (a) certain non-linear relationships bet

129 blem computationally infeasible for standard greedy optimization algorithms that account simultaneous

130 on a small collection of large graphs where greedy optimization is not applicable.

131 corresponding node and link community with a greedy optimization of a local community function conduc

132 networks small enough for the application of greedy optimization so that results from this algorithm

133 edian set partitioning problem and propose a greedy optimization technique.

134 We compare its simulation results with the greedy optimized link state routing (G-OLSR) and the opt

135 els like the top-scoring pairs model and the greedy pairs model, as well as standard methods includin

136 erican and Latino arrays utilizing rounds of greedy pairwise SNP selection, followed by removal from

137 The East Asian array was designed using greedy pairwise SNP selection.

138 ction by either a Brute Force Algorithm or a Greedy Partition Algorithm.

139 We define a reverse greedy path and show both analytically and numerically t

140 ith GraphMap, a software program that uses a greedy path searching algorithm, supplemented with local

141 Greedy propensity score matching was performed for organ

142 based sampling is often unable to outperform greedy sampling in Bayesian optimization.

143 The work introduces a greedy scheduler generating compact box placements.

144 Greedy scheduling appears as a simple generic route to o

145 re MBIL with the BN learning algorithms Fast Greedy Search (FGS), PC algorithm (PC), and CPC algorith

146 formulation and a computationally efficient greedy search algorithm called MultiDCoX to perform mult

147 omly chosen marmoset call features, we use a greedy search algorithm to determine the most informativ

148 In the last stage of modal exploitation, a greedy search strategy is used to accelerate the converg

149 constructs the optimal input sequence from a greedy search, and defines the associated optimal measur

150 bines stochastic sampling and deterministic 'greedy' search steps into a novel hybrid iterative schem

151 a ranking function and using a deterministic greedy selection algorithm or by using the leverage scor

152 aches have utilized either Gibbs sampling or greedy strategies to identify such elements in sets of s

153 h the set of approved applications, then any greedy strategy is optimal for her: She should submit an

154 CI estimates a finite mixture model, using a greedy strategy to gradually select error-free sequences

155 Based on a bottom-up greedy strategy, we further extend LCP(2) to a new algor

156 with large mutation supplies adapt via the "greedy" substitution of the fittest genotype available,

157 ome this bias, we propose Fast Interpretable Greedy-Tree Sums (FIGS), which generalizes the Classific

158 ithms were explored, with fast interpretable greedy-tree sums selected as the final machine-learning

159 tem based on the newly published Unfair Semi-Greedy (USG) algorithm, Earliest Deadline First (EDF), a

160 ted two algorithms, Greedy and Bidirectional Greedy, using curated protein sequence data from the ThY

161 Moreover, they are greedy variants of recently developed matrix algorithms

162 orithms: Focused Metropolis Search (FMS) and greedy-WalkSAT (G-WalkSAT) for random 3-SAT.