ライフサイエンスコーパス: cluster analyses

1 e the information among neighboring SNPs for cluster analyses.

2 ion-based subgroups of CRC using model-based cluster analyses.

3 ccus maripaludis using phylogenetic and gene cluster analyses.

4 ints was analyzed by logistic regression and cluster analyses.

5 for coherence using correlation, factor, and cluster analyses.

6 f pairwise serum-virus vector distances, and cluster analyses.

7 such as principal component and hierarchical clustering analyses.

8 rol subjects for differential expression and clustering analyses.

9 timised using the bidimensional hierarchical clustering analyses.

10 compiled TSG and OCG profiles and performed clustering analyses.

11 Chip hybridization protocol and hierarchical clustering analyses.

12 correlations (CSC) on correlation-based gene-clustering analyses.

13 Using multivariate statistical and cluster analyses, an ensemble of randomly oriented parti

14 mRNA expression clustering refined prior clustering analyses and identified a poor-survival "neur

15 e used to complete unsupervised hierarchical clustering analyses and identify differentially expresse

16 Results of cluster analyses are also presented, demonstrating the p

17 While phylogenetic and cluster analyses are often used to define clonal groups

18 Gene ranking and graph clustering analyses are integrated into the package.

19 bour distance, and regularity index, we used cluster analyses based on changes in soma size and round

20 om each geographical locale were analyzed by cluster analyses based on genetic fingerprinting with th

21 Cluster analyses based on nearest-neighbor distance (F,

22 Clustering analyses based on these outlier loci indicate

23 tors that undergo some degree of spontaneous clustering, analyses based on multivalence-mediated coop

24 training and technical support to farmers." Cluster analyses, based on participant opinions, grouped

25 Results of precomputed gene clustering analyses can be retrieved in tabular or graph

26 Cluster analyses explored whether NCEP identifies a mixt

27 High genetic diversity, nonconcordance of cluster analyses from different genetic loci, and shared

28 Cluster analyses grouped risk loci into five major categ

29 Hierarchical clustering analyses grouped samples according to their l

30 Cluster analyses have enhanced understanding of the hete

31 Detailed serum and HIV-1 isolate cluster analyses have shown that in general, the identif

32 Further, clustering analyses have identified seven major conforma

33 In addition, these cluster analyses identified calpactins I and II as novel

34 Principal component and hierarchical cluster analyses identified gene signatures unique to ea

35 biting different forest blocks, and Bayesian cluster analyses identified hierarchical structure.

36 Local cluster analyses identified Iowa and Northeastern Missou

37 A combination of outlier and cluster analyses identified putative new members of the

38 1) cotton using double feature selection and cluster analyses identified species-specific and stage-s

39 Cluster analyses identified three distinct profiles of n

40 Clustering analyses identified novel sRNA seed regions a

41 Clustering analyses identified other LEC2-induced RNAs n

42 e and population differentiation, as well as clustering analyses, indicate that the genomes of indivi

43 allergic diseases based primarily on recent cluster analyses, molecular phenotyping, biomarkers, and

44 Based on quantitative cluster analyses of 52 constitutively expressed or behav

45 r membership coefficients from 100 replicate cluster analyses of 600 chickens from 20 different breed

46 Hierarchical cluster analyses of clinical and pathologic features of

47 m cellular inflammation profiles, as well as cluster analyses of clinical variables and molecular and

48 Cluster analyses of folding trajectories showed the nati

49 gic significance of results obtained through cluster analyses of gene expression data generated in mi

50 In addition, cluster analyses of gene expression profiles among these

51 edia of Genes and Genomes pathway enrichment cluster analyses of microarray data using wild-type and

52 Separate cluster analyses of multilocus SB and single locus PCR-R

53 Principal component and cluster analyses of ROI volumes were used to identify pa

54 ternative to conventional data reduction and cluster analyses of similarity matrices that is rooted i

55 Cluster analyses of six independent samples of 100+ neur

56 sonance imaging, voxel-based morphometry and cluster analyses of the pathological groups here suggest

57 Pair correlation and cluster analyses of the resulting PALM images identified

58 As a result, replicate cluster analyses of the same data may produce several di

59 Factor and cluster analyses of the sputum cytokine profiles reveale

60 Cluster analyses of these spectra showed that UVRR spect

61 Pathway and annotated cluster analyses of those probe sets predicted that enti

62 Cluster analyses of transcriptional profiling data were

63 Similar cluster analyses of two published, independent data sets

64 Notably, statistical and clustering analyses of echinoid temporal gene expression

65 alternative option for biologists to perform clustering analyses of gene expression patterns or trans

66 Clustering analyses of gene-specific protein-mRNA ratios

67 However, clustering analyses of microsatellite and Chd1 gene sequ

68 like domains provides a platform for further clustering analyses of sequence similarities among diffe

69 ore similar than observations from different clusters, analyses of such data must take intracluster c

70 l subgroups were determined using factor and cluster analyses on 18 sputum cytokines.

71 ts, histograms, expression profile plots and cluster analyses plots.

72 Cluster analyses revealed 25 putatively preserved patien

73 Comparative genomics cluster analyses revealed novel gene families (clusters)

74 Cluster analyses revealed that for half the sample, incr

75 Cluster analyses revealed that most DE genes annotated a

76 Cluster analyses revealed that most taxonomic groups cou

77 Cluster analyses revealed two main groups of genetically

78 Cluster analyses showed that many of the transcripts wer

79 Bayesian-clustering analyses showed two distinct population clust

80 69 single nucleotide polymorphisms (SNPs) in clustering analyses, the Suriname sample appears sister

81 phase of TGA together with connectivity and cluster analyses to detect changes in the episodic memor

82 on, it describes a novel use of hierarchical cluster analyses to identify protein relatedness based o

83 ethodologies such as principal component and cluster analyses to provide a geometric representation o

84 titions that might be hidden when performing clustering analyses using all available genes.

85 Tree diagrams constructed by cluster analyses, using eight length factors in a given

86 Also, using principal component and cluster analyses, we determined a strong negative relati

87 eters, and, by using principal component and cluster analyses, we quantitatively categorized neurons

88 Spectral band area analysis and hierarchical cluster analyses were performed to clarify if the 2 peri

89 Both parsimony and cluster analyses were used to divide the genus into two

90 Principal component and cluster analyses were used to find patterns of cytokine/

91 tional enrichment, gene network, and k-means clustering analyses were used to identify molecular path

92 rred using principal components and Bayesian cluster analyses, were consistent with three genetic clu

93 Hierarchical clustering analyses with 3288 genes clearly segregated t

94 classes examined, no single copy would bias clustering analyses with regard to other closely related