ライフサイエンスコーパス: analysis method

1 th conventional differential gene expression analysis method.

2 l of reactivity and the energy decomposition analysis method.

3 ity of the technique by using a Bland Altman analysis method.

4 mistic view of the performance of an RNA-seq analysis method.

5 from hip DXA scans using the hip structural analysis method.

6 of the well-established over-representation analysis method.

7 on experimental data by a recently developed analysis method.

8 ith different labels, imaging techniques and analysis methods.

9 limitation to all previous patient-specific analysis methods.

10 in the experimental conditions and the data analysis methods.

11 ausal effect estimates from different causal analysis methods.

12 mprovement that complements current survival analysis methods.

13 39%) of those, authors used recommended meta-analysis methods.

14 an eBayes, DEseq and some commonly used meta-analysis methods.

15 sting estimates, and illustrate value in new analysis methods.

16 rscores the need for sensitive and selective analysis methods.

17 igh quality and processed using standardised analysis methods.

18 otal mass, or subunit mass with conventional analysis methods.

19 plicated across two studies and a variety of analysis methods.

20 ese outliers would have been missed by other analysis methods.

21 imb motor cortex (M1) using multiple circuit-analysis methods.

22 uman variation and by researchers developing analysis methods.

23 d individual patient data multivariable meta-analysis methods.

24 med design of experiments and choice of data analysis methods.

25 cible software algorithms, and reliable data analysis methods.

26 hierarchical cluster and principal component analysis methods.

27 ch can be easily obtained using multivariate analysis methods.

28 s metadata detailing experimental design and analysis methods.

29 rential expression, and alternative splicing analysis methods.

30 n methods including mixed models and linkage analysis methods.

31 th and sampling methods), and different data analysis methods.

32 al synchronization and time-lapse cell cycle analysis methods.

33 Data were combined using standard meta-analysis methods.

34 ghted Cox regression and random effects meta-analysis methods.

35 f detection of direct mass spectrometry (MS) analysis methods.

36 ckage that implements several published NIPT analysis methods.

37 reens have been limited by the use of manual analysis methods.

38 ch, scientists are in need of efficient data analysis methods.

39 licated by a lack of standard processing and analysis methods.

40 evaluated between groups with multivariable analysis methods.

41 with pooled analysis and random-effects meta-analysis methods.

42 d, PhyloSNP application was created with two analysis methods 1) a quantitative method that creates t

43 l of reactivity and the Energy Decomposition Analysis methods, a detailed quantitative understanding

44 contamination controls, and developed a data analysis method aiming to provide maximum throughput and

45 Rapid progress in single-cell analysis methods allow for exploration of cellular diver

46 Using our psi analysis method along with mutational varphi analysis, w

47 portant aspects in the development of an HRM analysis method and describes how HRM data are analysed

48 n results, we apply multivariate statistical analysis methods and 3D reconstruction approaches origin

49 he framework can be combined with other data analysis methods and can help assess their relative meri

50 cyanostar, by means of biological structure analysis methods and compared results to traditional sma

51 and sensitivity in other single-cell protein analysis methods and constitutes a versatile tool for th

52 Correlations were determined between MRE analysis methods and fibrosis stage.

53 Although across all analysis methods and modalities alterations in the PFC/A

54 Bivariate analysis methods and multivariate generalized linear reg

55 in nature, complements the existing gene set analysis methods and provides a promising new direction

56 The different laboratory analysis methods and sampling protocols resulted in inco

57 these problems is best done by improving our analysis methods, and by finding complementary models th

58 veness of spatial coverage, comparability of analysis methods, and consistency in sampling protocols.

59 data systems, enhancing data collection and analysis methods, and strengthening the research and pra

60 st Fourier transformation (FFT) is the major analysis method applied to calculate the spatial frequen

61 While many classical meta-analysis methods are developed with the former goal in m

62 In this paper, several image analysis methods are evaluated for their ability to accu

63 Existing ASE analysis methods are limited by a dependence on haplotyp

64 onary relationships between viruses, network analysis methods are more productive than traditional ph

65 espondingly, high-throughput automated image analysis methods are necessary to work on par with the s

66 et, implying that probabilistic phylogenetic analysis methods are needed.

67 Most pathway analysis methods are not designed for mechanistic rewiri

68 electron microscopy, and quantitative image analysis methods are now providing some of the first thr

69 RNA sequencing analysis methods are often derived by relying on hypothe

70 ic components remains a challenge, and novel analysis methods are required to reveal such features an

71 Though the acquisition and analysis methods are still evolving, new disease insight

72 To increase detection power, gene level analysis methods are used to aggregate weak signals.

73 ssessment, scenario modeling and sensitivity analysis methods are used to identify critical factors f

74 ty or electronic instabilities so distortion analysis methods are useful.

75 y classes to biological entities, enrichment analysis methods assess whether there is a significant o

76 ynapses, we have developed new recording and analysis methods at single central glutamatergic synapse

77 while an in-house established data-dependent analysis method based on high-resolution mass spectromet

78 econvolution, silencing and modular response analysis methods based on optimizing for sparsity, trans

79 In this paper, we propose a class of meta-analysis methods based on summaries of weighted ordered

80 Single-reaction analysis methods based on the exponential growth equatio

81 this work, we propose a set of three pathway analysis methods based on the impact analysis, that perf

82 rom applying the conventional flow-injection analysis method, based on the same type of reaction.

83 as received significant interest as an miRNA analysis method because of high amplification efficiency

84 plosive growth in the number of quantitative analysis methods being proposed.

85 sing both quantitative and molecular genetic analysis methods, both approaches lack studies specific

86 described in the study offers an alternative analysis method by enabling high speed analysis and the

87 rpose To describe a nonlinear finite element analysis method by using magnetic resonance (MR) images

88 tematically and objectively evaluate pathway analysis methods by employing any number of datasets for

89 an conventional differential gene expression analysis methods by integrating information at both gene

90 emonstrate the performance of different meta-analysis methods by using both simulated and empirical d

91 The novel fluctuation network analysis method can be used as a general strategy in stu

92 It is shown that super-resolution image analysis methods can significantly improve counting accu

93 eproducible pleiotropic effects.Multivariate analysis methods can uncover the relationship between ph

94 pically using sparse sampling and population analysis methods, can facilitate optimal dose selection

95 rrelation spectroscopy is a well-established analysis method capable of extracting the average size a

96 value of information and portfolio decision analysis-methods commonly applied in financial and opera

97 uld be difficult to detect with differential analysis methods comparing individual genes.

98 In general, our ultrastructural analysis methods could be useful for a wide range of cel

99 These image analysis methods describe how to virtually extract key c

100 For this reason, there have been many meta-analysis methods designed to combine gene expression dat

101 The Ra-226 analysis method developed in this study requires several

102 The statistical analysis methods developed in the conduct of this study

103 aramount importance for extending complex IR analysis methods developed in the far-field, e.g., for c

104 OINTS: Classic motor unit (MU) recording and analysis methods do not allow the same MUs to be tracked

105 Existing heritability analysis methods do not discriminate between stable effe

106 Current gene set analysis methods do not facilitate comparing gene sets a

107 Current genetic ITH analysis methods do not preserve spatial context and may

108 Although commonly used trend analysis methods do not show any trend in concurrent dro

109 s manuscript reports a new pesticide residue analysis method employing a microflow-liquid chromatogra

110 Image analysis methods enable quantitative study of the proper

111 Emerging computational analysis methods, especially in single-cell RNA sequenci

112 A fixed-effect meta-analysis method estimated differences in on-treatment av

113 experimental data, up-to-date RNA-sequencing analysis methods, expression data at the level of gene s

114 Here we demonstrate that the alignment-free analysis method feature frequency profiling (FFP) can be

115 Here, we present a new open source cluster analysis method for 3D SMLM data, free of user definable

116 nstrate a rigorous theoretical formalism and analysis method for computing the induced curvature fiel

117 This work describes an interferometry data analysis method for determining the optical thickness of

118 A total reflection X-ray fluorescence (TXRF) analysis method for direct compositional characterizatio

119 k-Assisted Target Identification), a network analysis method for drug target identification in haploi

120 , followed by a different untargeted UPLC-MS analysis method for each extract.

121 mass spectrometry method as the first direct analysis method for FTACPs.

122 strate a novel, voxel-based correlative data analysis method for in-depth evaluation of amyloid PET a

123 The proposed methodology could be used as an analysis method for the authentication of Spanish PDO wi

124 Here, we present a matrix analysis method for the evaluation of these systems and

125 again emerging as an important and powerful analysis method for the identification of genes involved

126 nalysis (MA1) model was the most appropriate analysis method for this PET radioligand in this species

127 Using a novel analysis method for time series data we identify transcr

128 The study describes a DW-MRI analysis method for tracking the progression of SD and p

129 (STAR) program: "Measurement, Modeling, and Analysis Methods for Airborne Carbonaceous Fine Particul

130 this question, we developed acquisition and analysis methods for blood-oxygen-level-dependent (BOLD)

131 n unmet need of fast and sensitive multiplex analysis methods for disease specific protein monitoring

132 rameter optimization methods and uncertainty analysis methods for MA and SSE.

133 lties associated with the currently employed analysis methods for marine toxins are encouraging the r

134 Current conventional and even advanced data analysis methods for MFC data explore only a subset of t

135 application of unbiased computational image analysis methods for morphodynamic quantification is rar

136 by umbrella sampling and Weighted Histogram Analysis Methods for multiple ions traversing the select

137 The service incorporates analysis methods for the identification of functional re

138 P-TAZ and the potential of using alternative analysis methods for the identification of new flame ret

139 mats emitted by SAMtools and introduced more analysis methods for variant analysis, including alterna

140 thms to develop computational predictors and analysis methods for various tasks in bioinformatics and

141 ary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way

142 l of reactivity and the energy decomposition analysis methods has been applied to gain a quantitative

143 Pathway analysis methods have a broad range of applications in p

144 Although several statistical analysis methods have been proposed for semi-competing r

145 Although many gene set analysis methods have been proposed to explore associati

146 While existing analysis methods have been useful, they are not without

147 Some automated analysis methods have emerged but do not robustly accoun

148 straints related to experimental designs and analysis methods have so far prevented the disentangleme

149 The latest sequencing and analysis methods have successfully identified somatic al

150 e-set algorithm and found that two different analysis methods identified distinct gene-set signatures

151 s combining the output from multiple genomic analysis methods in an intuitive and interactive manner.

152 Analysis methods in cognitive neuroscience have not alwa

153 ta using complex workflows to developing new analysis methods in common languages such as Python, R,

154 les that must be performed using traditional analysis methods in order to calculate relative expressi

155 , SDEAP was able to outperform the other DTE analysis methods in our extensive experiments on simulat

156 ive doses) during BL DPT, using the survival analysis method, in order to suggest optimal step doses.

157 transcripts over other published expression analysis methods, in both synthetic data and qPCR/NanoSt

158 Despite advances in the gene-set enrichment analysis methods; inadequate definitions of gene-sets ca

159 d possible results make traditional ChIP-Seq analysis methods inappropriate for use with replication

160 Analysis methods included nonlinear least-squares fittin

161 Our analysis method includes input from both protein-protein

162 rk topology was characterized by three graph analysis methods including the commonly-used weighted an

163 nnorm also performs better than existing DEG analysis methods, including BASiCS, NODES, SAMstrt, Seur

164 ractical drawback of many P-value-based meta-analysis methods, including Fisher's, Stouffer's, minP a

165 These six different analysis methods indicate that the average extent of rea

166 The third analysis method is a hybrid of these two models.

167 achieve the target, the over-representation analysis method is divided in four different steps and,

168 Thus, it is important that a 4C-Seq analysis method is flexible enough to analyze data gener

169 Therefore, a UHPLC-HR-Orbitrap-MS analysis method is optimized and validated for the quant

170 ed by a single primer pair, a directed graph analysis method is used to identify minimum amplicon til

171 A simultaneous residue analysis method is validated using QuEChERS extraction w

172 et, (18)F-FDG PET with advanced discriminant analysis methods is able to accurately distinguish ALS f

173 In addition, the analysis method must be carefully considered, as this ch

174 secondary organic aerosol (SOA) composition analysis methods normalizing aerosol yield and chemical

175 Q-Gen is an extension to the gene set analysis method of QuSAGE, and allows for linear mixed m

176 We designed a novel, non-spectral-analysis method of separating ultradian from circadian c

177 nic IHC and the AQUA (Automated Quantitative Analysis) method of QIF.

178 ement is critical in several disciplines but analysis methods often neglect key information by adopti

179 We applied a recently developed multi-trait analysis method on a small set of bacteria hypersensitiv

180 Current analysis methods or sensors for research and application

181 that the three experimental optimization and analysis methods (orthogonal design, uniform design and

182 research; one of the main problems, from the analysis methods performance point of view, is the const

183 ct to one of the most popular cell migration analysis methods, Persistent Random Walk, we found that

184 single cell culture and transcript abundance analysis method presented here provides the tools necess

185 The manipulation and analysis methods presented in our study may also be help

186 Integrative network analysis methods provide robust interpretations of diffe

187 All of these analysis methods provide the user with supporting statis

188 This direct analysis method represents an accurate, advantageous alt

189 Common analysis methods require many steps including extraction

190 However, most existing analysis methods require that the binding motifs are enr

191 The most widely used analysis method requires manual clustering through indiv

192 The shipping conditions, analysis methods, results, and laboratory performance we

193 Chemometric analysis methods revealed superior beans for macro and m

194 A combination of metadynamics and committor analysis methods reveals how this reaction can change fr

195 modeling, Screening Bayesian Evaluation and Analysis Method (ScreenBEAM).

196 strategy, reference database and downstream analysis methods selection can have a dramatic effect on

197 Here we provide a survey of data analysis methods starting from an overview of basic stat

198 Our analysis method (STROMA4) assigns a score along each str

199 Instrumental analysis method such as GC/MS is sensitive but not rapid

200 series analysis or in conjunction with other analysis methods such as tracking.

201 An alternative analysis method, such as the use of linear models (with

202 ery flexible and allows a wide range of meta-analysis methods, such as the random effects model, to a

203 Current analysis methods tend to ignore this correlation by test

204 We present a flux balance analysis method that allows for the computation of dynam

205 e introduce CC-PROMISE as an integrated data analysis method that combines components of canonical co

206 Here, we present a new data analysis method that combines mechanistic stochastic mod

207 tion based on deep X-ray data and a new data analysis method that enable us to evaluate directly the

208 he trapping time and by implementing a novel analysis method that improves the signal-to-noise ratio

209 Here, we developed a single-cell genome analysis method that reconstructs genome-wide haplotype

210 tranded RNA viruses, we have developed a new analysis method that reveals the asymmetric structural o

211 Herein, we present a simple graphical analysis method that takes advantage of the data-rich re

212 We then developed an analysis method that tracks dynamic changes in large-sca

213 ta should be supported by interactive visual analysis methods that allow a scientist to understand fu

214 ) in situ hybridization and spectral imaging analysis methods that allow simultaneous detection of mu

215 constitutes a new generation of genomic data analysis methods that allow studying variants found in n

216 and operation and demonstrate vulnerability analysis methods that are applicable to a wider class of

217 r demonstrates the value of using rule-based analysis methods that can identify subgroups with hetero

218 Analysis methods that capitalize on slopes in a single r

219 We present image analysis methods that determine the order and geometry o

220 e interest in developing pathway and network analysis methods that group genes and illuminate the pro

221 However, analysis methods that optimize statistical power through

222 o be suitable for the evaluation of all data analysis methods that utilize the proper experimental de

223 A. thaliana as well as improvements in data analysis methods that we anticipate will further enhance

224 uctured decision-making (SDM) with portfolio analysis, methods that have been used independently to e

225 by conventional differential gene expression analysis method, the top 10 significant genes selected b

226 , due to the shortage of convenient sampling/analysis methods, the analysis of sweat has not become v

227 proposed differential regulation enrichment analysis method, though exploratory in nature, complemen

228 -based immunofluorescence assay and an array analysis method to achieve simultaneous, sensitive and v

229 pathways identified by applying a particular analysis method to an original large dataset and those i

230 ercome these limitations by developing a new analysis method to detect the replay of temporal pattern

231 transfer and a, to our knowledge, novel data analysis method to directly measure the end-to-end dista

232 Here we show a conceptually new data analysis method to enable operando visualization of mech

233 We use a finite-size analysis method to identify the type of phase transition

234 l approach as a highly sensitive statistical analysis method to identify transcriptional signatures t

235 n efficient variational inference and a post-analysis method to improve the accuracy and speed of ide

236 Eigenstrat uses a principal component analysis method to model all sources of sampling variati

237 We used a multicriteria decision analysis method to prioritise antibiotic-resistant bacte

238 maldehyde, and developed an isotope dilution analysis method to quantitate these organosulfur compone

239 We developed a novel local alignment vector analysis method to quantitatively measure collagen fiber

240 In this study, we use a variety of sequence analysis methods to compare all available sequence data

241 current with emerging omics technologies and analysis methods to continue supporting novel hypothesis

242 ror rate in the raw read data requires novel analysis methods to deconvolute sequences derived from c

243 vector machine [SVM] approach) (18)F-FDG PET analysis methods to differentiate ALS from controls in a

244 g massive amounts of data, necessitating new analysis methods to discover the biological and computat

245 can be processed by multivariate statistical analysis methods to enable rapid species-level identific

246 We used non-parametric survival analysis methods to estimate gains in the population-wid

247 We use extended cost-effectiveness analysis methods to estimate, across income quintiles, t

248 We used univariate and multivariate fMRI analysis methods to examine the role of AnG in multimoda

249 alize data and to apply advanced data mining analysis methods to explore the data and draw biological

250 e bladder and breast tumors, and use network analysis methods to identify the tumor type-specific cor

251 e high-dimensional and require computational analysis methods to interpret.

252 achine learning and multivariate statistical analysis methods to provide further partitioning between

253 udy, we investigated several pharmacokinetic analysis methods to quantify changes in ERbeta availabil

254 ate the ability of state-of-the-art sequence analysis methods to specifically recover known cancer ge

255 he inability to easily apply a wide range of analysis methods to the plethora of different datasets a

256 We developed image acquisition and analysis methods to track single particles that interact

257 uce the general transition-based reweighting analysis method (TRAM), a statistically optimal approach

258 netic variants was used with three different analysis methods, two of which (MR-Egger and the weighte

259 t methods is not dependent on the particular analysis method used to identify perturbed gene sets.

260 ake could be found, irrespective of the data analysis method used.

261 imates is not affected by the tracer kinetic analysis method used.

262 AP, a fast, multiplexed sub-synaptic protein analysis method using wide-field data from deconvolution

263 In this study two extraction and UHPLC-HRMS analysis methods, valuable for evaluation of consumer ac

264 The primary analysis method was a negative binomial regression, with

265 The proposed analysis method was applied to an example time stretch i

266 The semiautomatic analysis method was better than the fully automatic meth

267 The prespecified survival analysis method was competing risk regression.

268 For the study presented here, an uncertainty analysis method was developed and used to calculate the

269 As an alternative, a fast untargeted analysis method was developed that uses direct analysis

270 A nonlinear finite element analysis method was developed to estimate mechanical par

271 A discriminant analysis method was employed in the classification proce

272 Fisher's linear discriminant analysis method was employed to classify awake and drows

273 A graphical stability analysis method was employed to determine the stabilizin

274 An improved data analysis method was implemented with the combination of

275 An evolutionary concept analysis method was selected for this analysis.

276 position by translation and rotation (SITAR) analysis method was used to define the mean trajectories

277 reported variable-time normalization kinetic analysis method was used to delineate the complex reacti

278 IR) spectroscopy, combined with multivariate analysis methods, was applied in order to monitor extra

279 magnitude of variation, observed using both analysis methods, was highly dependent on the overall ex

280 Using multivariate analysis methods we examined the short-time evolution of

281 By using a novel deep proteomic analysis method, we identify 638 individual high-confide

282 To develop and test single-synapse image analysis methods, we have used datasets from conjugate a

283 italizing on recent advances in multivariate analysis methods, we used nonnegative matrix factorizati

284 Spectral analysis methods were developed to obtain IR spectra fro

285 Stimulus characteristics and analysis methods were not associated with the findings o

286 Parameters derived by the arterial analysis methods were strongly correlated with one anoth

287 Random-effects meta-analysis methods were used to estimate proportions.

288 Standard cost-benefit analysis methods were used to estimate the costs and ben

289 Image analysis methods were used to measure penetration depth

290 with the one-dimensional weighted histogram analysis method (WHAM) was used to calculate the diffusi

291 ance to other mixed model confounding factor analysis methods when identifying such eQTL.

292 usions were (1) to employ established sample analysis methods, when possible, and alternate methodolo

293 ble approaches to this problem are (i) using analysis methods which take advantage of different featu

294 stem for measuring GA by combining an enzyme analysis method, which is already used in clinical pract

295 propose a joint sparse canonical correlation analysis method, which uses a generalized fused lasso pe

296 Gene set analysis methods, which consider predefined groups of ge

297 This imaging and analysis method will be of value to other researchers an

298 neutralization studies by the median-effect analysis method with H77.16 and broadly reactive HMAbs r

299 sed to develop a region of interest-free DXA analysis method with increased spatial resolution for as

300 bility of single-family and joint-family QTL analysis methods with fivefold cross-validation for 6 di