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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

21 A greedy algorithm was used to improve the efficiency.

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

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

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

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

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

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

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

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

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

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

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

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

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

35 Existing greedy and stochastic algorithms are not guaranteed to f

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

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

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

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

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

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

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

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

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

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

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

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

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

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

50 While a greedy consensus algorithm, which consecutively accepts

51 matrix representation with parsimony and the greedy consensus.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

71 speed comparable to implementations based on greedy methods.

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

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

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

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

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

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

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

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

80 Greedy scheduling appears as a simple generic route to o

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

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

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

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

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

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

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

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