ライフサイエンスコーパス: dimensionality reduction

1 ew collective coordinates by using nonlinear dimensionality reduction.

2 sian ideal observer method for task-specific dimensionality reduction.

3 An important tool in information analysis is dimensionality reduction.

4 .8 x 10(-5) [FDR </=0.05], P for multifactor dimensionality reduction = 5.9 x 10(-45)).

5 SIPI has higher power than MDR (Multifactor Dimensionality Reduction), AA_Full, Geno_Full (full inte

6 AMBROSIA was compared with multifactor dimensionality reduction across several diverse models a

7 rformed Eigenanatomy, a statistically robust dimensionality reduction algorithm, and used leave-one-o

8 f any investigation, we recently developed a dimensionality reduction algorithm, sketch-map, that can

9 erages ensemble classification theory within dimensionality reduction, allowing for application to a

10 For comparison, we performed the same dimensionality reduction analyses on the activity of a r

11 urvival analysis, similar gene detection and dimensionality reduction analysis.

12 rs1143634) was assessed using multifactorial dimensionality reduction analysis.

13 ms existing methods including multifactorial dimensionality reduction and Bayesian epistasis associat

14 orithm that combines kernel-based non-linear dimensionality reduction and binary classification (or r

15 A combination of longitudinal, dimensionality reduction and categorical analysis of the

16 Subsequent multifactor dimensionality reduction and classification and regressi

17 Using unsupervised dimensionality reduction and clustering algorithms, we i

18 th data analysis based on diffusion maps for dimensionality reduction and network synthesis from stat

19 o mathematical approaches to simplification, dimensionality reduction and the maximum entropy method,

20 tein conformational search, efficient robust dimensionality reduction and topological analysis via pe

21 Dimensionality reduction and visual exploration facilita

22 accepted role of PS membranes as providing "dimensionality reduction" and favor a regulatory role fo

23 estimate the model's parameters, DREISS uses dimensionality reduction, and identifies canonical tempo

24 risons with the relevance-chain, multifactor dimensionality reduction, and PDT methods, the results f

25 risons with the relevance-chain, multifactor dimensionality reduction, and the pedigree disequilibriu

26 plicability of our method in an unsupervised dimensionality reduction application by inferring genes

27 This novel dimensionality reduction approach is broadly applicable

28 This observation justifies the use of dimensionality reduction approaches to model complex sys

29 In this paper, a novel algorithm, Dimensionality Reduction based Optimization for DOT (DRO

30 The dimensionality reduction brings the promise of a decreas

31 he algorithm to perform joint clustering and dimensionality reduction by efficiently optimizing a con

32 e few relevant coordinates emerging from the dimensionality reduction can correctly identify the tran

33 eate tree structures, whereas algorithms for dimensionality-reduction create low-dimensional spaces.

34 k has many important implications, including dimensionality reduction, differential diagnosis, and es

35 Dimensionality reduction (DR) enables the construction o

36 ssification, as against using the method for dimensionality reduction (DR).

37 ndependent marker sets, extended multifactor-dimensionality reduction (EMDR) analysis was employed to

38 ributions, regularly confront the problem of dimensionality reduction: finding meaningful low-dimensi

39 lying protein interactions and new tools for dimensionality reduction for flexible protein docking.

40 LCF uses nonlinear dimensionality reduction for pattern recognition.

41 The proposed model uses dimensionality reduction for preprocessing the Likes dat

42 lear, however, how the results obtained from dimensionality reduction generalize to recordings with l

43 approach, namely the generalized multifactor dimensionality reduction (GMDR) method, which can entert

44 Dimensionality reduction has been applied in various bra

45 researchers who seek to understand the role dimensionality reduction has had and can have in systems

46 riate data raises the fundamental problem of dimensionality reduction: how to discover compact repres

47 Multifactor dimensionality reduction identified a gene-gene interact

48 ere assessed through model-based multifactor dimensionality reduction in the PIAMA study, and gene-ge

49 Dimensionality reduction in the training set established

50 Multifactor dimensionality reduction indicated that the best genetic

51 Parallel multifactor dimensionality reduction is a tool for large-scale analy

52 Visualizing data by dimensionality reduction is an important strategy in Bio

53 The goal of dimensionality reduction is to embed high-dimensional da

54 To interpret the outputs of dimensionality reduction, it is important to first under

55 Unlike clustering methods for local dimensionality reduction, LLE maps its inputs into a sin

56 The computationally efficient multifactor dimensionality reduction (MDR) approach has emerged as a

57 We introduce multifactor-dimensionality reduction (MDR) as a method for reducing

58 Multifactor Dimensionality Reduction (MDR) has been introduced previ

59 Multifactor dimensionality reduction (MDR) is a powerful model-free

60 , we have previously developed a multifactor dimensionality reduction (MDR) method for collapsing hig

61 his problem, we have developed a multifactor dimensionality reduction (MDR) method for collapsing hig

62 natorial approaches, such as the multifactor dimensionality reduction (MDR) method, have emerged as a

63 natorial approaches, such as the multifactor dimensionality reduction (MDR) method, the combinatorial

64 s, a novel approach based on the multifactor dimensionality reduction (MDR) method, to detect genetic

65 We used Multifactor- dimensionality reduction (MDR) program as a non-parametr

66 re investigated by entropy-based multifactor dimensionality reduction (MDR), classification and regre

67 CE algorithm was compared to the multifactor dimensionality reduction (MDR), generalized MDR (GMDR),

68 onlinear stochastic embedding (One-SENSE), a dimensionality reduction method based on the t-distribut

69 We demonstrate that the choice of dimensionality reduction method can significantly influe

70 We highlight the importance of selecting a dimensionality reduction method to visualize large multi

71 ining an extended self-organizing maps-based dimensionality reduction method with bootstrap-based non

72 ly developed a higher-order statistics based dimensionality reduction method, called quasi-anharmonic

73 Here, we show that combining a nonlinear dimensionality reduction method, t-statistic Stochastic

74 Here, we develop a dimensionality-reduction method, (Z)ero (I)nflated (F)ac

75 We use two dimensionality reduction methods as well, robust Princip

76 We test the performance of these dimensionality reduction methods by applying several goo

77 We describe the dimensionality reduction methods commonly applied to pop

78 However, existing dimensionality reduction methods could not be directly a

79 me this limitation, we introduce model-based dimensionality reduction methods for neurons with non-Po

80 However, existing dimensionality reduction methods often either fail to se

81 Recent studies have applied dimensionality reduction methods to understand how the m

82 We introduce several dimensionality reduction methods to visualize in 2- and

83 Discovery of efficient and accurate dimensionality reduction methods used to display at once

84 , we use normalized edit distance to enhance dimensionality reduction methods, including Isomap and L

85 data has motivated continuous development of dimensionality reduction methods.

86 l RNA-seq data are challenging for classical dimensionality-reduction methods because of the prevalen

87 Stimulus dimensionality-reduction methods in neuroscience seek to

88 e-scale numerical simulation with nonlinear "dimensionality reduction" methods.

89 clinical variables (Model 1), and after PCA dimensionality reduction (Model 2).

90 ing techniques that include fast, non-linear dimensionality reduction of oligonucleotide signatures a

91 that functional groups of spines defined by dimensionality reduction of receptive field properties e

92 Dimensionality reduction of such high-dimensional data s

93 ecomes constrained, effectively leading to a dimensionality reduction of the learning problem; at the

94 Alternatively, one can perform dimensionality reduction on the output trajectory and ob

95 ly be a byproduct of MSTd neurons performing dimensionality reduction on their inputs.

96 ly be a byproduct of MSTd neurons performing dimensionality reduction on their inputs; and (3) imply

97 ontrast to previous algorithms for nonlinear dimensionality reduction, ours efficiently computes a gl

98 indings can help guide the interpretation of dimensionality reduction outputs in regimes of limited n

99 mponent analysis (PCA) is a popular tool for dimensionality reduction, pattern recognition, and visua

100 Here we describe an approach to solving dimensionality reduction problems that uses easily measu

101 al benchmarking on tasks such as single cell dimensionality reduction, protein module discovery and r

102 a network of profiles based on the nonlinear dimensionality reduction results.

103 Multifactor dimensionality reduction revealed that genotyping result

104 red the scaling trends of two key outputs of dimensionality reduction-shared dimensionality and perce

105 Cluster-wise dimensionality reduction should make it feasible to impr

106 on of metastable states, along with dramatic dimensionality reduction, significantly simplifies the t

107 t solutions to common problems: the need for dimensionality reduction, strategies for topographic or

108 bjective of this work was to develop a novel dimensionality reduction technique as a part of an integ

109 Using a dimensionality reduction technique known as non-negative

110 oy the diffusion map approach as a nonlinear dimensionality reduction technique to extract a dynamica

111 Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal co

112 Mash extends the MinHash dimensionality-reduction technique to include a pairwise

113 nd on these findings by pursuing more robust dimensionality reduction techniques based on manifold em

114 However, dimensionality reduction techniques can provide approxim

115 nt Analysis (PCA) is one of the most popular dimensionality reduction techniques for the analysis of

116 Clustering and dimensionality reduction techniques often help in discer

117 equires the use of the latest clustering and dimensionality reduction techniques to automatically seg

118 hen focus on one widely used set of methods, dimensionality reduction techniques, which allow users t

119 se of a greater level of information through dimensionality reduction techniques.

120 eature analysis provides a general two-sided dimensionality reduction that reveals encoding in high d

121 haracterizations adequately reflect the true dimensionality reduction that takes place in the nervous

122 Many recent studies have adopted dimensionality reduction to analyze these populations an

123 ps such as peak alignment, normalization, or dimensionality reduction to be tailored to specific prob

124 We develop a new approach to dimensionality reduction: tree preserving embedding.

125 ual-algorithm that uses cluster analysis and dimensionality reduction using a cohort of randomly sele

126 FITF also outperformed multifactor dimensionality reduction when interactions involved addi

127 ribe ACCENSE, a tool that combines nonlinear dimensionality reduction with density-based partitioning