ライフサイエンスコーパス: gene selection

1 ticipate in conferring specificity of target gene selection.

2 on (QIF) framework was applied for efficient gene selection.

3 sufficient to strongly influence p53 target gene selection.

4 sed using conventional methods for on-target gene selection.

5 WNT/p21 circuit is driven by C-clamp target gene selection.

6 nces in DNA specificities can dictate target gene selection.

7 ce of a differentially expressed pathway and gene selection.

8 thms which represent the stat-of-the-art for gene selection.

9 taneous cluster selection and within cluster gene selection.

10 thout the requirement for a priori candidate gene selection.

11 important for reducing bias due to adaptive gene selection.

12 ess the potentially large bias introduced by gene selection.

13 This paper describes a gene selection algorithm based on Gaussian processes to

14 Shevade and Keerthi propose a gene selection algorithm based on sparse logistic regres

15 is crucial to develop a simple but efficient gene selection algorithm for detecting differentially ex

16 erimental results show that the mRMR-ReliefF gene selection algorithm is very effective.

17 mplements leading pattern classification and gene selection algorithms and incorporates cross-validat

18 , BLogReg and Relevance Vector Machine (RVM) gene selection algorithms are evaluated over the well-st

19 Gene selection algorithms for cancer classification, bas

20 rion idea of Chen et al., we propose two new gene selection algorithms for general Bayesian models an

21 ing and comparing new projection methods and gene selection algorithms.

22 Although gene selection and cancer classification are two closely

23 provide a unified procedure for simultaneous gene selection and cancer classification, achieving high

24 Importantly, the implementation integrates gene selection and class prediction stages, which is vit

25 he Bayesian model averaging (BMA) method for gene selection and classification of microarray data.

26 Typical gene selection and classification procedures ignore mode

27 Grid (caBIG) analytical tool for multiclass gene selection and classification.

28 with the optimum combination of classifier, gene selection and cross-validation methods, we performe

29 self-organizing maps, and modules for marker gene selection and heat map visualization that allow use

30 Standard gene selection and pathway analysis methods were applied

31 mprove the results of downstream statistical gene selection and pathway identification methods.

32 nome, then reveal tarsier-specific, positive gene selection and posit population size changes over ti

33 The data analysis, gene selection and prediction approaches permitted group

34 cerGenes resource to simplify the process of gene selection and prioritization in large collaborative

35 cells of interest without the constraint of gene selection and the ambiguous nature of data obtained

36 atical programming approach is developed for gene selection and tissue classification using gene expr

37 NA binding, subcellular localization, target gene selection and transcriptional activity of Ets prote

38 Our objective is to understand p53 target gene selection and, thus, enable its optimal manipulatio

39 ificance analysis of microarrays is used for gene selection, and a multivariate linear regression mod

40 nalytic tool, iterative clustering and guide-gene selection, and clonogenic assays to delineate hiera

41 used for gene function prediction, candidate gene selection, and improving understanding of regulator

42 The VIP gene selection approach could identify additional subset

43 The VIP gene selection approach was compared with the p-value ra

44 A new hybrid gene selection approach was investigated and tested with

45 Two common methods for gene selection are: (a) selection by fold difference (at

46 r data union and intersection, and candidate gene selection based on evidence in multiple datasets or

47 e test error rates to models based on single gene selection, but are more sparse as well as more stab

48 R5 interaction provides plasticity in target gene selection by MYC and speculate on the biochemical a

49 for network construction, module detection, gene selection, calculations of topological properties,

50 f beta 2 on alpha 3, alpha 3 gene and V beta gene selection can be correlated.

51 Informative gene selection can have important implications for the i

52 neuroligin, in addition to mRNA splicing and gene selection, contributes to the specificity of the ne

53 Gene selection differences were also noted in L chains.

54 The mechanisms that regulate variable (V) gene selection during the development of the mouse IgH r

55 in both the BM and spleen, suggesting that V-gene selection events correlate with CD23 expression in

56 y, PCA projection facilitated discriminatory gene selection for different tissues and identified tiss

57 t the Affymetrix software and to rationalize gene selection for experimental designs involving limite

58 ds of genes and the small number of samples, gene selection has emerged as an important research prob

59 Advances in understanding target gene selection have been hampered by the lack of genes k

60 iscuss the implication of this Zipf's law on gene selection in a microarray data analysis, as well as

61 near Discriminant Analysis (ELDA) to address gene selection in a multivariate framework.

62 By doing so, the HHSVM performs automatic gene selection in a way similar to the L(1)-norm SVM.

63 phosphorylation and subsequent PR-B-specific gene selection in coordination with STAT5.

64 a general framework to incorporate feature (gene) selection into pattern recognition in the process

65 It is yet unclear to what extent Hox target gene selection is dependent upon other regulatory factor

66 We find that for a given data set gene selection is highly repeatable in independent runs

67 vector machines both in the settings when no gene selection is performed and when several popular gen

68 In general, however, gene selection may be less robust than classification.

69 This paper proposes a novel gene selection method with rich biomedical meaning based

70 A novel gene selection method, POS, is proposed.

71 Therefore, robust and accurate gene selection methods are required to identify differen

72 ection is performed and when several popular gene selection methods are used.

73 cross-platform comparisons and the impact of gene selection methods on the reproducibility of profili

74 rithm is more accurate than state-of-the-art gene selection methods that are particularly developed t

75 ms for multicategory classification, several gene selection methods, multiple ensemble classifier met

76 We apply POS, along-with four widely used gene selection methods, to several benchmark gene expres

77 e accurate results than the state-of-the-art gene selection methods.

78 Our data indicate that gene selection, mRNA splicing, and post-translational mo

79 We based candidate gene selection on bioinformatics, reverse transcription-

80 Gene selection on those pathways identified 58 genes in

81 d with classification models based on single gene selections, our rules are stable in the sense that

82 sed on gene expression profiles suggest that gene selection plays a key role in improving the classif

83 However, the genes selected from one binary gene selection problem may reduce the classification per

84 FE (called MSVM-RFE) to solve the multiclass gene selection problem, based on different frameworks of

85 Many approaches have been proposed for this gene selection problem.

86 -RFE was originally designed to solve binary gene selection problems.

87 t shrink centroids and uses a class-specific gene-selection procedure.

88 Very little attention has been given to gene selection procedures based on intergene correlation

89 ducibility of DEG lists of a few widely used gene selection procedures.

90 At each step of the gene selection process, the functional relevance of the

91 reveals the molecular basis of unique target gene selection/recognition, DNA binding cooperativity, a

92 ensitivity, reproducibility and stability of gene selection/sample classification to the choice of pa

93 everaging the structure of gene networks for gene selection, so that the relationship information bet

94 taneously considering all classes during the gene selection stages, our proposed extensions identify

95 The gene selection step identified three novel genes (RbAp48

96 Because of the gene selection step, test statistic in SPCA model can no

97 Since survival information is used in the gene selection step, this method is semi-supervised.

98 As outcome information is used in the gene selection step, this method is supervised, thus cal

99 xteme value distributions to account for the gene selection step.

100 A feature (gene) selection step, however, must be added to penalize

101 Here, we report effective gene selection strategies to identify potential driver g

102 tion algorithms which used simple univariate gene selection strategy and constructed simple classific

103 his, we developed an effective computational gene selection strategy that represents public data abou

104 odeling, supervised/unsupervised informative gene selection, supervised/unsupervised data visualizati

105 Gene selection techniques can significantly improve the

106 lts on chromosome 2q to prioritize candidate-gene selection, thus identifying SERPINE2 as a positiona

107 t direct Myc's recruitment to DNA and target gene selection to elicit specific cellular functions hav

108 Various computational models rely on random gene selection to infer such networks from microarray da

109 gnificant results in the current experiment, gene selection using an a priori hypothesis (neurodevelo

110 The fundamental problem of gene selection via cDNA data is to identify which genes

111 To achieve dimension reduction and gene selection, we decompose each gene pathway into a si

112 nd human microarray data to inform candidate-gene selection, we observed significant family-based ass

113 Successful gene selection will help to classify different cancer ty