ライフサイエンスコーパス: running time

1 ers with 52K redundant genes in 1.5 hours of running time.

2 r on the number of considered candidates and running time.

3 co-clustering based method has advantages in running time.

4 tion time, which is the dominant part of the running time.

5 an other state-of-the-art tools with shorter running time.

6 rnative gain measure is also given to reduce running time.

7 twork search, which dramatically reduces the running time.

8 greatly reduces computational complexity and running time.

9 its per query, significantly speeding up the running time.

10 er programs with significant improvements in running time.

11 The fast screening algorithm reduced 98% of running time.

12 ed HMMs in bounded memory without increasing running time.

13 hile being the fastest in terms of the total running time.

14 he-art shows reduced memory requirements and running times.

15 savings in cache misses reduce the empirical running times.

16 er quality than competing methods in shorter running times.

17 ignificantly outperforms existing methods in running time, accuracy, or both.

18 ity in mice, as evidenced by their increased running time and distance.

19 lary-to-fibre ratio (C/F), increased maximal running time and elevated basal expression of VEGF and m

20 At the same time, its moderate running time and low memory footprint allow metaBEETL to

21 Additionally, running time and memory requirements are about constant

22 eck in assembly pipelines, and improving its running time and memory usage is an important problem.

23 cy of the predictions, we bench-marked HAP's running time and phasing accuracy against PHASE.

24 the performances of our approach in terms of running time and quality of the alignments using the BAl

25 notypes to analyse has little effect both on running time and required memory.

26 he correct network and compares favorably in running time and results with methods based on value of

27 We demonstrate an improved trade-off between running time and retrieval accuracy, controlled by the s

28 the methods using d-separable matrices while running time and solution size are comparable.

29 ategy does not work due to rapid increase in running time and space usage.

30 e existing methods both in the computational running time and the metastability.

31 INRICH has wide applicability, fast running time and, most importantly, robustness to potent

32 -to-use graphical user interface (GUI), fast running times and automated parameter discovery.

33 Our experimentation compares the running times and distance efficiency of Lloyd's K-means

34 ing all possible elements severely increases running times and more importantly the chance for false

35 s' lengths are above 100 residues, excessive running times and sub-optimal energy functions.

36 fferences with respect to alignment quality, running time, and usability in general.

37 In addition, the running times are 30-40% faster for M-ZDOCK.

38 For example, the running time can be reduced by 20-30% while achieving RO

39 lted in a further 326% increase in endurance running time compared with the performance level of mice

40 een described, but many are hampered by long running times, confounding of selection and recombinatio

41 Running times differ substantially between methods.

42 in runners were similar across quintiles of running time, distance, frequency, amount, and speed, co

43 e data, significant differences in the total running time, equilibrium moisture content, sorption hys

44 thus they may exhibit sharp fluctuations in running time, especially for large alphabets.

45 often not well-defined on categorical data; running time for computations using high dimensional dat

46 ther sacrifice optimality or trade increased running time for reduced memory.

47 rograms) to achieve an effectively quadratic running time for simultaneous pairwise alignment and fol

48 the Bayesian prior on node ages reduces the running time for this computation on the 349 taxa datase

49 arkov Models, we achieve drastically reduced running times for Bayesian inference using Forward-Backw

50 roved performance by >100-fold, reducing the running time from hours to mere minutes for typical jobs

51 is a complex computational problem in which running time grows exponentially with the number of mani

52 memory (at least 8 GB), but whose asymptotic running time has never been theoretically established.

53 ems: (i) high false-positive rate; (ii) long running time; (iii) work only for genomes in their datab

54 The running time increases with the size of the genetic data

55 ASTRAL's running time is [Formula: see text], and ASTRAL-II's run

56 time is [Formula: see text], and ASTRAL-II's running time is [Formula: see text], where n is the numb

57 ernative is qscore, a method whose empirical running time is approximately the same as FastSP when gi

58 DECOD uses a k-mer count table and so its running time is independent of the size of the input set

59 Elsewhere, the running time is no greater than O(mn(m+n)).

60 m is based on fractional programming and its running time is O(n2log n).

61 the algorithm is output sensitive, i.e. its running time is quasi-linear to the size of the generate

62 is discussed theoretically, and the program running time is reported for various test examples.

63 lying generic tree distance measure and fast running time make MulRF useful for inferring phylogenies

64 th one exception, the algorithm has expected running time O(mn).

65 e of the SEEDY algorithm is that it is fast, running time O[(E + V) log V] for V proteins and E inter

66 We studied the running time of 47 problems generated from 17 data sets.

67 4-well plate can be achieved with unattended running time of 5.4h.

68 ion suggests that (i) our method reduced the running time of a single query on a database of around 3

69 ACompress program significantly improves the running time of all previous DNA compression programs.

70 parameter tuning and, surprisingly, quickest running time of all the tried methods.

71 sting tools, PSAMM significantly reduced the running time of constraint-based analysis and enabled fl

72 Unfortunately, the running time of EM is linear in the length of the input

73 This algorithm has a worst-case running time of O(n(2)) which is inferior to two previou

74 e present further improvements that make the running time of our algorithm practical.

75 We provide an analysis of the expected running time of the algorithm and demonstrate that STEME

76 problem in polynomial time, which means the running time of the algorithm is a polynomial function o

77 The running time of the algorithm is dependent on the scorin

78 motif consists of multiple elements and the running time of the algorithm is highly dependent on the

79 Moreover, the running time of the cloud implementation for our method

80 y and locally related sequence sets, and the running time of the program is considerably improved.

81 y more than 50%, and this leads to a reduced running time of the quantification tool.

82 We report running times of 127 s to find all SSRs of length 20 bp

83 we find strategies that previously required running times of days or more.

84 and empirical analysis demonstrated a linear running-time of the algorithm, which is the fastest appr

85 task, which can require hundreds of hours of running time on a single CPU even for the fastest known

86 with other methods in terms of accuracy and running time on both simulated and real data, and our ex

87 n engineered carefully to achieve remarkable running times on regular PCs.

88 exact algorithms with exponential worst-case running time or heuristics that do not guarantee optimal

89 th BTSC and MOTSC demonstrated a much faster running time over exhaustive search with the same accura

90 has comparable power to and a much improved running time over previous methods, especially in detect

91 s leads to improvements both in accuracy and running time over the alternative, which is to run a bin

92 our index achieves a significant speed-up in running time over the state-of-the-art methods such as C

93 rofile of Mood States post-BCT and in faster running time (P < 0.05) in volunteers reporting to BCT w

94 ld gel electrophoresis (PFGE), is slow, with running times ranging from 10 hours to more than 200 hou

95 The highly reproducible data including the running time, real-time sample mass, target relative hum

96 tional optimization methods with the average running time reduced by as much as 80% and with optimali

97 pplication of a step stimulus, the bacterial running time relaxes to its pre-stimulus level.

98 The algorithm is iterative, and its running time requirements are proportional to T.N.(d.(lo

99 an expectation-maximization algorithm whose running time scales linearly with the number of observed

100 CloudBurst's running time scales linearly with the number of reads ma

101 In addition, DFBAlab's running time scales linearly with the number of species

102 Unfortunately, MEME's running time scales poorly with the size of the dataset.

103 educes common artifacts associated with long running times, such as blurred bands and comingling of c

104 to each sequencing run, while maintaining a running time that is within the range of practical use.

105 To optimize running time, the code uses clustering and multi-threadi

106 ) or its vehicle at 7 days prior to tests of running time to exhaustion were evaluated in 60-day-old

107 tion of striatal DA, produces a reduction in running time to exhaustion.

108 Finally, the running time to fit the model grows linearly in the numb

109 rformance comes at the expense of increasing running time to O(N(2)), rendering BP prohibitive for mo

110 MA resulted in significantly decreased running times to exhaustion compared to vehicle-treated

111 This makes usability and running time two additional important features of our me

112 Total running time was 11 min.

113 Two-mile running time was assessed post-BCT; mood was assessed by

114 Pre-treatment running times were not significantly different between t

115 he FFAS server was also optimized for speed: running times were reduced by an order of magnitude.

116 mapping methods need more memory and longer running time when larger maximum deletion size is chosen

117 Using a cache-oblivious kd-tree, we realize running times, which match the state-of-the-art.