ライフサイエンスコーパス: gene selection

1 thout the requirement for a priori candidate gene selection.

2 important for reducing bias due to adaptive gene selection.

3 ess the potentially large bias introduced by gene selection.

4 ticipate in conferring specificity of target gene selection.

5 eves effective scRNA-seq data clustering and gene selection.

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

7 sufficient to strongly influence p53 target gene selection.

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

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

10 nces in DNA specificities can dictate target gene selection.

11 ce of a differentially expressed pathway and gene selection.

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

13 taneous cluster selection and within cluster gene selection.

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

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

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

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

18 we describe a machine learning-based marker gene selection algorithm, NS-Forest version 2.0, which l

19 y developing a neural network-based feature (gene) selection algorithm called Wx.

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

21 Although many feature/gene selection algorithms and methods have been introduc

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

23 Gene selection algorithms for cancer classification, bas

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

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

26 Although gene selection and cancer classification are two closely

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

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

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

30 Typical gene selection and classification procedures ignore mode

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

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

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

34 One such issue is feature or gene selection and identifying relevant and non-redundan

35 Through gene selection and machine learning, five new potential

36 oys a two-stage manifold fitting process for gene selection and noise reduction, followed by agglomer

37 Standard gene selection and pathway analysis methods were applied

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

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

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

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

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

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

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

45 obial community composition shape resistance gene selection and transmission processes.

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

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

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

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

50 Multi-View Multi-Objective clustering-based gene selection approach called UMVMO-select.

51 The VIP gene selection approach could identify additional subset

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

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

54 h or with analysis of variance (ANOVA)-based gene selection approach.

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

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

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

58 ally, scPNMF modifies the PNMF algorithm for gene selection by changing the initialization and adding

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

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

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

62 Informative gene selection can have important implications for the i

63 clude the dearth of proven selectable marker gene-selection combinations and tissue culture methods f

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

65 isoforms II-IV using the developed Bayesian Gene Selection Criterion (BGSC) approach.

66 Gene selection differences were also noted in L chains.

67 ms developed for single-cell RNA-seq adopted gene selection, dimension reduction or imputation to add

68 downstream analytical tasks such as variable gene selection, dimensional reduction, and differential

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

70 tes erroneous subdivisions, increases marker-gene selection efficacy, and efficiently scales to milli

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

72 enes, thereby outperforming state-of-the-art gene selection/extraction methods for clustering.

73 hlighting the importance of stable reference gene selection for accurate expression normalization and

74 roposed a hybrid novel technique CSSMO-based gene selection for cancer classification.

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

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

77 $R{\prime}{e}nyi$ and $Tsallis$ entropies in gene selection for single cell clustering.

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

79 Therefore, interest in robust gene selection has gained considerable attention in rece

80 Feature gene selection has significant impact on the performance

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

82 ep algorithm, Iterative Clustering and Guide-gene Selection (ICGS), which applies intra-gene correlat

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

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

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

86 shows that epithelioids recapitulate mutant gene selection in aging human esophagus and identifies a

87 Cancer Portal, a comprehensive catalogue of gene selection in cancer based purely on the biochemical

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

89 -mRNA sampling on gene expression and marker gene selection in single-cell and single-nucleus RNA-seq

90 Gene selection in the portal is quantified by combining

91 A new cancer portal quantifies and presents gene selection in tumor over the entire human coding gen

92 arily on high mutation rates as evidence for gene selection in tumors.

93 e of phylogenetic variables including marker gene selection, inference methods, corrections for rate

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

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

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

97 During rearrangement, Ab gene selection is mediated by factors that influence chr

98 HK gene selection is often arbitrary, potentially introduci

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

100 Reporter gene selection is vital to a sensor performance and appl

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

102 stering has been applied within our proposed gene selection method to satisfy both incorporated views

103 The proposed gene selection method with DL achieves much better class

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

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

106 oduce sc-REnF [robust entropy based feature (gene) selection method], aiming to leverage the advantag

107 By employing a "Few-Shot Genes Selection" method, we randomly select smaller subs

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

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

110 Even though many gene selection methods have been developed for scRNA-seq

111 A limitation of existing gene selection methods is their reliance on scRNA-seq da

112 that scPNMF outperforms the state-of-the-art gene selection methods on diverse scRNA-seq datasets.

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

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

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

116 Compared with existing gene selection methods, scPNMF has two advantages.

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

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

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

120 situ horizontal gene transfer of resistance genes; selection of pre-existing resistance; and immigra

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

122 Gene selection on those pathways identified 58 genes in

123 Most current approaches to marker gene selection operate in a label-based framework, which

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

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

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

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

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

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

130 multivariate rank-distance correlation-based gene selection procedure (MrDcGene) to LUAD multi-omics

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

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

133 ion methods, 10 reference datasets, 5 marker gene selection procedures, 5 data normalizations and 2 t

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

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

136 ild-type SOX18 protein, including its target gene selection process.

137 -based correlation information (iTwiner) for gene selection produced the best classification results

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

139 hetic datasets, we find that anti-correlated gene selection reduces or eliminates erroneous subdivisi

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

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

142 As an alternative tool for unbiased HK gene selection, software tools exist but are limited to

143 The method integrates information-theoretic gene selection, spatially weighted likelihood modeling,

144 a robust statistical approach for reference gene selection, stable genes selected from RNA-Seq data

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

146 The gene selection step identified three novel genes (RbAp48

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

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

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

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

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

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

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

154 l strategy, we propose a hierarchical marker gene selection strategy that groups similar cell cluster

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

156 e Cross-Magnitude-Altitude Score (Cross-MAS) gene selection strategy, which integrates results across

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

158 O was applied to a high-dimensional leukemia gene selection task, where it identified ultra-compact s

159 Gene selection techniques can significantly improve the

160 e gpsFISH, a computational method performing gene selection through optimizing detection of known cel

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

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

163 n gene expression analysis is to improve hub gene selection to enrich for biological relevance or imp

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

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

166 We performed gene selection using regularized regression and develope

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

168 hat for datasets with large sample size, the gene-selection via the Wilcoxon rank sum test (a non-par

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

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

171 Successful gene selection will help to classify different cancer ty

172 verall reliable performance in single marker gene selection, with COSG showing commendable speed and