ライフサイエンスコーパス: analysis method

1 les were subjected to the developed two-step analysis method.

2 ls were compared using a surface-based shape analysis method.

3 h extracts and quantified by a digital image analysis method.

4 th conventional differential gene expression analysis method.

5 l of reactivity and the energy decomposition analysis method.

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

7 on experimental data by a recently developed analysis method.

8 prick dried blood spot collection kit and an analysis method.

9 ggest optimal step doses, using the survival analysis method.

10 e two-level individual participant data meta-analysis method.

11 protein-protein interaction data and network analysis methods.

12 t BEAPR largely outperforms often-used count analysis methods.

13 rface for searching through experimental and analysis methods.

14 ck of relevant high-throughput synthesis and analysis methods.

15 Swish, with popular differential expression analysis methods.

16 variates are not uncommon, and demand robust analysis methods.

17 ring to other linear models and coexpression analysis methods.

18 trometry, antiviral tests, and proposed data analysis methods.

19 enable direct comparison of these different analysis methods.

20 biases, and missed insights from traditional analysis methods.

21 ith different labels, imaging techniques and analysis methods.

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

23 uman variation and by researchers developing analysis methods.

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

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

26 evaluated between groups with multivariable analysis methods.

27 screening designs, and advanced statistical analysis methods.

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

29 datasets using malacoda and alternative MPRA analysis methods.

30 e currently unaccounted by conventional soil analysis methods.

31 compare this method to other mutation burden analysis methods.

32 a is foundational for benchmarking Hi-C data analysis methods.

33 ied using traditional fixed-effect (FE) meta-analysis methods.

34 ing and the development of common downstream analysis methods.

35 ng conditions using conventional time series analysis methods.

36 that are too sparse for current single-cell analysis methods.

37 traits measured in >26,900 mice, using meta-analysis methods.

38 to achieve because of the lack of effective analysis methods.

39 amily history encoded in HOGs and python HOG analysis method, a python library for programmatic proce

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

41 Although many variant analysis methods accept VCF as input, many other tools r

42 using an extensive suite of target-chemical analysis methods along with a variety of biological effe

43 o address this, we introduce interactive HOG analysis method, an interactive JavaScript widget to vis

44 This study aims to optimize the analysis method and determine a more sensitive cut-off l

45 provide guidance in choosing an appropriate analysis method and introduce key features of the newest

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

47 evolution platform, and provides new tools, analysis methods and datasets to study m(1)A biology.

48 improvements of the principal on-line breath analysis methods and evaluates obstacles for their wider

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

50 Here, we use advanced computational analysis methods and high-field human fMRI data to resol

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

52 The coupling of different analysis methods and modeling approaches we developed in

53 performance of differential gene expression analysis methods and need to be considered in terms of t

54 By improving existing contractility analysis methods and overcoming technical challenges ass

55 chnology to complement more traditional salt analysis methods and provide insights into systems-level

56 shed data sets led us here to reevaluate the analysis methods and raw data of published SH2-pTyr HTP

57 ing flow cytometry, traditional gating-based analysis methods and support the data by employing bioin

58 Several analysis methods and visualization tools have been devel

59 aightforward integration of multi-population analysis, method and sample-based concordance metrics, a

60 roduce the UWHAM (binless weighted histogram analysis method) and SWHAM (stochastic UWHAM) software p

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

62 populations, diagnostic criteria, microbiota analysis methods, and reporting on different taxonomic l

63 ified, it is important to ensure established analysis methods are adopted.

64 Preliminary unsupervised multivariate analysis methods are also included to provide rapid insi

65 gh data are ever more plentiful and powerful analysis methods are available, there remain many challe

66 However, current analysis methods are cumbersome and lack the use of the

67 Several analysis methods are employed to reveal changes in the s

68 New technologies and analysis methods are enabling genomic structural variant

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

70 can improve forecasts, standard time series analysis methods are inadequate to estimate all the para

71 Furthermore, several new analysis methods are included.

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

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

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

75 Standardized EDA data analysis methods are readily available.

76 Furthermore, existing analysis methods are relatively low-throughput, indirect

77 ust counterparts, robust genetic interaction analysis methods are significantly less popular but are

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

79 ought to understand how portable current ONT analysis methods are.

80 ur C++ implementation of the direct-coupling analysis method as a standalone software package.

81 ime deliver those as soon as possible, rapid analysis methods as well as sensitive, reliable, cost-ef

82 ormation systems (GIS)-based slope stability analysis method assuming a normal stress distribution ac

83 The new fractal dimension-based data analysis method automatically detected audio sections wi

84 t comprehensive comparative study on pathway analysis methods available to date.

85 We developed an acoustic data analysis method based on active sound production by larv

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

87 developed a novel differential accessibility analysis method based on information gain to identify th

88 In this study, we evaluated two NTA data analysis methods based on maximum-likelihood estimation,

89 Analysis methods based on simulations and optimization h

90 We have used a surfaceome analysis method, based on comparing RNA-seq data between

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

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

93 Here we introduce this image analysis method by presenting its biophysical foundation

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

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

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

97 -cell and bulk samples so that developers of analysis methods can assess accuracy and precision.

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

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

100 in HepG2 renders traditional genome variant analysis methods challenging and partially ineffective.

101 ns of Gly were obtained, and a range of data analysis methods compared, leading to a detection limit

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

103 iable benchmark against which future pathway analysis methods could and should be tested.

104 per, we propose a new differential abundance analysis method, DASEV, which uses an empirical Bayes sh

105 However, a joint analysis method designed to integrate DNMs from multiple

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

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

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

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

110 Non-linear analysis methods (e.g., entropy) have been explored to c

111 ntified through application of numerous data analysis methods, each developed to characterize a speci

112 However, current ChIP-exo analysis methods either treat all binding events as bein

113 One important category of analysis methods employs an enrichment score, which is c

114 tion speed, together with more complex image analysis methods, facilitate tackling biological problem

115 , GSA, and GSEA combined with classical meta-analysis methods (Fisher's and the additive method), plu

116 Most multi-trait analysis methods focus on individual common variants in

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

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

119 spectroscopy (RICS) is a fluorescence image analysis method for extracting the mobility, concentrati

120 Here, we developed a new 4D analysis method for filopodial dynamics and a data-drive

121 We propose DENDRO, an analysis method for scRNA-seq data that clusters single

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

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

124 des practical tools and examples of selected analysis methods for a functional MRI dataset and multiv

125 cal and health applications, choosing proper analysis methods for biomarker identification remains a

126 lore the applicability of several complexity analysis methods for characterization of non-linear aspe

127 s an alternative to traditional sampling and analysis methods for measuring aqueous Hg(II) concentrat

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

129 performance of differential gene expression analysis methods for scRNAseq data.

130 site Toxoplasma gondi and describe optimised analysis methods for small scale libraries.

131 Two types of analysis methods for SPR images are used to study the pr

132 lking agents has placed an emphasis on trace analysis methods for their detection from complex drug m

133 However, high-dimensional mediation analysis methods for time-to-event outcome data are stil

134 Most computational analysis methods for visual attention utilize black-box

135 )) in coastal salt marshes using dimensional analysis method from fluid mechanics and hydraulic engin

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

137 olecular dynamics simulation and normal mode analysis methods have become the gold standard for study

138 To date, many transcriptomic meta-analysis methods have been developed, yet few thoughtful

139 this type of analysis, more than 70 pathway analysis methods have been proposed so far.

140 Meta-analysis methods have been widely used to combine result

141 Gene co-expression network analysis methods have been widely used to identify corre

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

143 Both analysis methods identified two MRSA clusters based on r

144 ture-based molecular networking (FBMN) as an analysis method in the Global Natural Products Social Mo

145 o help scientists make an informed choice of analysis methods in a dataset-specific manner.

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

147 Analysis methods in cognitive neuroscience have not alwa

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

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

150 actual performance of 13 widely used pathway analysis methods in over 1085 analyses.

151 All the analysis methods in this assay are based on liquid chrom

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

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

154 Analysis methods included Kaplan-Meier and Cox proportio

155 id profiles employed in various multivariate analysis methods including principal component analysis

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

157 To this end, we utilize systems analysis methods including the pointwise mutual informat

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

159 Advanced brain imaging analysis methods, including multivariate pattern analysi

160 outbreaks, we evaluated various designs and analysis methods, including recently proposed methods fo

161 imental data were analyzed using statistical analysis methods, including the t-test and partial least

162 Finally, an uncertainty analysis method is also presented in order to evaluate t

163 In the present work, a barcode-DNA analysis method is described for the detection of plant

164 ds long time traces; however, an appropriate analysis method is missing.

165 athways (potentially in the same cells), the analysis method is parsimonious and intuitive, and kinet

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

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

168 mance of four frequently employed population analysis methods is assessed by comparisons with experim

169 ely used and one of the popular multivariate analysis methods is canonical correlation analysis (CCA)

170 Our novel image analysis method led to extraction of over 20 features.

171 tissue-compartment model and the multilinear analysis method (MA1) to calculate regional distribution

172 This review discusses the sequencing and analysis methods most appropriate for the study of the b

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

174 llect samples, which further complicates the analysis methods needed.

175 0 temperature runs by the weighted-histogram analysis method of heavy-atom, structure-based models of

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

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

178 d reliable technique namely optimal homotopy analysis method (OHAM).

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

180 udy, we developed a novel multiplex glycomic analysis method on an LC-ESI-MS platform.

181 pression data set and consequently benchmark analysis methods on this data set with a known ground tr

182 hensive review of robust genetic interaction analysis methods, on their methodologies and application

183 ew data integration strategies, and advanced analysis methods outlined in this review provide a frame

184 Moreover, we use the multivariate analysis method partial least squares that combines mult

185 Existing meta-analysis methods perform statistical tests on sets of pu

186 We also applied a separate protein-analysis method (protein topography and migration analys

187 This comparative hydrologic test analysis method provides a new way to quantify the amoun

188 A novel time-frequency entropic analysis method, referred to as Activation Complexity (A

189 There was full concordance between the two analysis methods regarding species (S. aureus), detectio

190 st current molecular dynamics simulation and analysis methods rely on the idea that the molecular sys

191 Unfortunately, traditional HSI analysis methods remain unable to rapidly process the vo

192 al features with cytometry data, traditional analysis methods require cell gating as an intermediate

193 Common analysis methods require many steps including extraction

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

195 t, we performed Mendelian randomization (MR) analysis.Methods: RNA sequencing was performed on whole-

196 We call an analysis method 'scale-invariant' (SI) if it gives the s

197 Here we introduce a model-based factor analysis method, SDA, to analyze a novel 57,600 cell dat

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

199 Comparison with state-of-the-art pathway analysis methods shows that BONITA has higher sensitivit

200 It is the first data analysis method specifically targeting Scarabaeidae larv

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

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

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

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

205 Genome-wide analysis methods, such as array comparative genomic hybr

206 ult demonstrates the need for extraction and analysis methods, such as the ones presented here, that

207 Furthermore, the data analysis methods suffer from multidimensionality, requir

208 While the unsupervised spectral analysis methods suggested a slight suppression of signa

209 l of reactivity and the Energy Decomposition Analysis method suggests that the enhanced reactivity of

210 plication of a multivariate curve resolution analysis method supplemented with quantum chemical calcu

211 FSI) and the Scientific Working Group on DNA Analysis Methods (SWGDAM) using 500 samples.

212 Current analysis methods tend to ignore this correlation by test

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

214 analysis (CIA) is a multivariate statistical analysis method that can assess relationships and trends

215 co-inertia analysis (mCIA) is a multivariate analysis method that can assess relationships and trends

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

217 A reproducible semiautomatic quantification analysis method that entailed mesencephalic intensity as

218 Seq) is an ethologically inspired behavioral analysis method that identifies modular components of th

219 We introduce GeneSurrounder, an analysis method that integrates expression data and netw

220 Here we describe an analysis method that provides data-driven estimates of t

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

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

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

224 er disease (AD) pathology burden clinically, analysis methods that enable tracking brain amyloid or t

225 ancement of experimental techniques and data analysis methods that has made it possible to also study

226 ludes an extensive set of commonly used data analysis methods that have been implemented using struct

227 However, analysis methods that integrate both types of data are l

228 Survival analysis methods that integrate pathways/gene sets into

229 Further, our review concludes that pathway analysis methods that target specific pathway properties

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

231 In combination with a unique image-based analysis method, the system enables full automation in t

232 igate, through novel usage of time-frequency analysis methods, the dynamics of hypoxia-induced PB in

233 Unlike other local orientation analysis methods, the E-CNA method allows for atomic sca

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

235 hydrophilic interaction chromatography-based analysis method to confirm that PerA is the only pathway

236 gh-resolution fluorescent PCR-based fragment analysis method to develop MultiFRAGing - a robust and c

237 This study uses the meta-analysis method to establish the link between binge drin

238 including 596 avian taxa), and applied a new analysis method to estimate the sensitivity of island-sp

239 We therefore developed a robust MRI analysis method to identify brain regions that correlate

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

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

242 place, we developed a fully automated image analysis method to measure quantitatively changes in bot

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

244 ve developed and applied a quantitative data analysis method to produce volumetric three-dimensional

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

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

247 ntal theta by applying advanced multivariate analysis methods to a multimodal MEG/EEG dataset.

248 e MSI data were analyzed using advanced data analysis methods to efficiently extract metabolomic info

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

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

251 ology terms, employing Fisher's Discriminant Analysis methods to identify previously unknown positive

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

253 motifs, and sites, in combination with novel analysis methods to interrogate different aspects of the

254 o present challenges in how to adapt current analysis methods to meet the increased scale.

255 unique new dataset and novel application of analysis methods to multiple relevant datasets, identifi

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

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

258 analysis side by side with other single-cell analysis methods, to illustrate when it is advantageous

259 Current scRNA-seq analysis methods typically overcome dropout by combining

260 Traditional qPCR analysis methods typically rely on paired designs.

261 sample cleanup, extraction, and quantitative analysis method using pyrolysis gas chromatography mass

262 Analysis methods using a globally estimated significance

263 ghput, single-cell, fluorescence-based image analysis method utilizing the Amnis ImageStream(X) instr

264 We applied unsupervised latent class analysis methods utilizing baseline clinical and biomark

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 gal staining, surface creation and biovolume analysis method was developed enabling visualization and

269 Therefore, an integrated analysis method was developed to quantify the petrochemi

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

271 A modified content analysis method was used to code responses and identify

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

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

274 Additionally a non-linear analysis method was used.

275 molecular dynamics followed by an automated analysis method, we discover and characterize previously

276 Using the genome-wide complex trait analysis method, we estimated the IHPS SNP heritability

277 eries of ordination, statistical and network analysis methods, we associated different life-history s

278 Using a variety of sequencing and analysis methods, we identified a wide spectrum of genom

279 sing peptide microarrays and tandem MS-based analysis methods, we show that the proline-rich stretch

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

281 SY) builds on our previously described ratio analysis method [ Wei et al.

282 Survival modeling and landmark analysis methods were applied to evaluate LTAD benefit o

283 Spectral analysis methods were developed to obtain IR spectra fro

284 ures ANOVA, network analysis, and enrichment analysis methods were employed to identify metabolites a

285 Over time, the design and analysis methods were further modified to allow estimati

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 Survival analysis methods were used to estimate the cumulative ri

289 New analysis methods were used to estimate the frequency and

290 Image analysis methods were used to measure penetration depth

291 Standard survival analysis methods were used.

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

293 MAIC out-performs other meta-analysis methods when using our CRISPR screen as validat

294 benefits from a precise signal recording and analysis method which leads to the detection of the pres

295 re variant (RV) non-parametric linkage (NPL) analysis method, which has advantages over association m

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

297 his limitation, this work introduces a novel analysis method, which is broadly applicable to any LPTV

298 al colorimetric and ion chromatographic (IC) analysis methods, which are unable to compensate for the

299 However, due to limitations of current analysis methods, which require manual or semi-manual ha

300 001) of the fully automated deep learning BC analysis method with manual segmentation.