ライフサイエンスコーパス: data analysis

1 action and sequencing, as well as downstream data analysis.

2 dimension reduction methods and longitudinal data analysis.

3 integrated to facilitate interactive genomic data analysis.

4 riments in parallel, and (5) straightforward data analysis.

5 ameters for fast statistical and exploratory data analysis.

6 re, and facilitates efficient, collaborative data analysis.

7 ed cell sorting, whole-genome sequencing and data analysis.

8 ul tools for complex microbiome/metagenomics data analysis.

9 tronics, and machine learning for predictive data analysis.

10 d model, called Chronnet, for spatiotemporal data analysis.

11 for applications of AutoML to biomedical big data analysis.

12 ity for the developers of algorithms for raw data analysis.

13 Here we apply robust statistics on RNA-seq data analysis.

14 nities and challenges in data collection and data analysis.

15 as well as interactive tools for exploratory data analysis.

16 ected, blood-based modeling was required for data analysis.

17 present guidelines for selecting methods for data analysis.

18 mparable models for metabolomic longitudinal data analysis.

19 is not supported by any systematic study or data analysis.

20 imitations in existing approaches to complex data analysis.

21 ithreading and indexing enable efficient big data analysis.

22 plicable for genome-wide scale time-to-event data analysis.

23 ble Python package for scRNA-seq exploratory data analysis.

24 ration, validation, example applications and data analysis.

25 ic workflows and command-line tools for bulk data analysis.

26 ust be supplied and rightfully belong in any data analysis.

27 the current pipelines to facilitate scRNAseq data analysis.

28 reated a need for user-friendly software for data analysis.

29 while highlighting challenges of metagenomic data analysis.

30 om these repositories to perform exploratory data analysis.

31 linical information and enhances comparative data analysis.

32 Three theme categories emerged from data analysis.

33 re used to create national estimates for all data analysis.

34 mall RNA'ome, a prerequisite for down-stream data analysis.

35 ak, greatly simplifying data acquisition and data analysis.

36 ing models and using a Bayesian approach for data analysis.

37 -based microscopy combined with an automated data analysis.

38 mating to sequencing and 7 d for sequencing data analysis.

39 facilitate database querying and real-world data analysis.

40 on and (ii) computation using Unix and R for data analysis.

41 creened, of whom 139 (80%) were eligible for data analysis.

42 parameter choices and (iv) post-experimental data analysis.

43 bolic geometry into the realm of single-cell data analysis.

44 he background noise is a fundamental step in data analysis.

45 nvolve their collaborators in the process of data analysis.

46 pectrometry method creation and quantitative data analysis.

47 2 d for sequencing preparation and 2-4 h for data analysis.

48 ed five different statistical approaches for data analysis.

49 em by Gaussian beam shaping and multivariate data analysis.

50 ect the most appropriate method in empirical data analysis.

51 d only needing freely available software for data analysis.

52 er scientific community is the complexity of data analysis.

53 ons from community ecology and compositional data analysis.

54 in genomic risk prediction, for multi-omics data analysis.

55 systems pharmacology approaches for patient data analysis.

56 as a summary table, is provided for further data analysis.

57 itate development of scalable algorithms for data analysis.

58 ayesian matrix factorization for single cell data analysis.

59 eneration sequencing (NGS) and bioinformatic data analysis.

60 trode and guide tube, neuronal recording and data analysis.

61 using the random effects model was used for data analysis.

62 metric data acquisition (2 d), and proteomic data analysis (1 week).

63 We overview (1) compositional data analysis, (2) data transformations and (3) network

64 cloud-based artificial intelligence (AI) for data analysis; (5) potential bias of interpretive algori

65 ach was implemented, comprising quantitative data analysis, a systematic literature review, creation

66 binding affinities, we introduce topological data analysis, a variety of network models, and deep lea

67 a Shiny-based application for linked -omics data analysis, allowing users to visualize microbial fun

68 Using longitudinal data analysis and controlling for duration of uveitis, p

69 However, the capacity for data analysis and counselling is already restricting the

70 computational strategies for nuclear RNA-seq data analysis and develop a new pipeline, Tuxedo-ch, whi

71 sease, PolyA-miner can significantly improve data analysis and help decode the underlying APA dynamic

72 a simple and effective algorithm for BSA-Seq data analysis and implemented it in Python; the program

73 re we introduce methods based on topological data analysis and interpretable machine learning for qua

74 To facilitate data analysis and interpretation based on MENDA, we also

75 will improve and standardize processing and data analysis and interpretation in functional and struc

76 mes; these effects must be considered during data analysis and interpretation.

77 dable tables and figures supporting flexible data analysis and interpretation.

78 o regulate the fate of PHCs currently limits data analysis and interpretations.

79 web interfaces were developed to facilitate data analysis and intuitively visualize results.

80 de process optimization, comprehensive omics data analysis and management have been a challenge.

81 oaches that tackle challenges in large-scale data analysis and management.

82 cing data, with additional time required for data analysis and mining.

83 Such an approach could limit the scope of data analysis and prevent us from uncovering new informa

84 y for researchers and educators to carry out data analysis and report the results in a single digital

85 tilization, therapeutic drug monitoring, and data analysis and reporting.

86 rometry (UV-LC-MS(2)) coupled with two-stage data analysis and spiked control.

87 ally evaluate who is served and neglected by data analysis and to center structural determinants of h

88 Data analysis and transformation steps can be run indivi

89 Although comprehensive intraoperative data analysis and transparent disclosure have been advoc

90 roducing a time window mechanism for dynamic data analysis and using machine learning method predicts

91 preparation (6-7 d), SPEED imaging (4-5 h), data analysis and validation through simulation (5-13 h)

92 onstrate the features of HiGwas through real data analysis and vignette document in the package.

93 However, data analysis and visualization from metabolic tracing s

94 egrated software has been made available for data analysis and visualization.

95 ment of new software libraries and tools for data analysis and visualization.

96 variety of R packages to provide interactive data analysis and visualization.

97 It takes 3 d plus 2 weeks for proteomics and data analysis and will enable the study of RBP dynamics

98 ct lifecycle, including experimental design, data analysis, and management (i.e., sharing and storage

99 importance of combining experiment, advanced data analysis, and molecular simulations.

100 Besides offering powerful techniques for data analysis, ANNs provide a new approach for neuroscie

101 We devised a multiomics data analysis approach based on Mendelian randomization

102 SOM-EFS as a powerful non-linear exploratory data analysis approach in the field of volatile analytic

103 A targeted data analysis approach, based on previous findings repor

104 Through our CAD-centered multiomics data analysis approach, we identified 33 molecular bioma

105 ng amounts of metagenomic data, the need for data analysis approaches that keep up with the pace of s

106 ribe the progress in the instrumentation and data analysis approaches undertaken for deciphering X-ra

107 Crucial components of screen design and data analysis are also discussed.

108 these to alternatives developed for RNA-seq data analysis are lacking.

109 er, protocols for RIBO-Seq and corresponding data analysis are not yet standardized.

110 These techniques of topological data analysis are scalable and could be used in studies

111 ly popular but available tools for automated data analysis are still limited.

112 oaches often do not integrate RNA-sequencing data analysis, are not automated or are restricted to us

113 thaliana) served as a reference organism for data analysis, as more than 200 genes have been associat

114 od that has not been used so far for medical data analysis at a large scale in comparison to other tr

115 M2IA streamlines the integrative data analysis between metabolome and microbiome, from da

116 ng has seen marked advances in detection and data analysis, but there is less progress in understandi

117 anticipate that coolpup.py will aid in Hi-C data analysis by allowing easy to use, versatile and eff

118 CIPR facilitates scRNAseq data analysis by annotating unknown cell clusters in an

119 roach can dramatically benefit automatic ECG data analysis by providing cardiologist level accuracy a

120 lysis: (i) takes the first step of proactive data analysis by utilizing available CPU power from the

121 ware provides built-in optimization for 'big data' analysis by storing all relevant outputs in an SQL

122 Experiments and data analysis can be performed over a period of 10-13 d

123 Existing solutions for these data analysis challenges (i.e., multivariate statistics

124 s were compared in this paper, compositional data analysis (CoDa) and classical statistical analyses,

125 their attention to materials preparation and data analysis, cognizant of the impact of defects.

126 urally motivates future efforts to interlink data analysis, computational modeling, and formal theory

127 The time required for data analysis depends on the complexity of the protein a

128 als, we conducted a wide-ranging exploratory data analysis (EDA), pooling our original data with exta

129 immense benefit in peak discovery to improve data analysis efforts.

130 ed for standardized strategies for NGS HIVDR data analysis, especially for the detection of minority

131 Systematic in silico data analysis followed by immunohistochemical validation

132 manuscript presents results from a secondary data analysis for 1,022 women aged 15-24 who reported ev

133 istics have been widely used in multivariate data analysis for outlier detection in chemometrics and

134 ion in association with Principal Component (Data) Analysis for craft beer classification.

135 dological advances, including 4D imaging and data analysis, for improved tracking of calcium flux in

136 is a powerful tool for posterior predictive data analysis, for methods validation and for teaching c

137 A data analysis framework depicts the individual evolution

138 tically encoded fluorescent biosensor, and a data analysis framework to quantify the recruitment kine

139 ries to germline genetics, the time-to-event data analysis has attracted increasing attention in the

140 Classical data analysis has been first performed on generated data

141 Recently, an updated approach to data analysis has been unveiled (version 5.0), replacing

142 Data analysis has focused on identifying dopamine from c

143 roposed system advances the field of genomic data analysis: (i) takes the first step of proactive dat

144 Normalization is an indispensable step of data analysis in all those studies, since it removes unw

145 hine learning for multivariate spectroscopic data analysis in applications related to process monitor

146 Large-scale data analysis in bioinformatics requires pipelined execu

147 We discuss strategies for real data analysis in face of uncertainty about the underlyin

148 inical management and for robust large-scale data analysis in healthcare research.

149 tic studies in animal models and large scale data analysis in human patients.

150 the initial steps of the common pipeline for data analysis in metabolomics.

151 Pooled data analysis in the field of maternal and child nutriti

152 a comprehensive approach to study design and data analysis in which questions guide the choice of app

153 15; eight men) underwent cardiac 4D flow MRI data analysis including calculation of peak systolic and

154 cular Pharmacology These revisions relate to data analysis (including statistical analysis) and repor

155 Multivariate statistical data analysis indicated that the metabolic profile chang

156 Financial data analysis indicates estimated annual cost savings to

157 The feed turns genomic data analysis into a collaborative work between the anal

158 RNA-seq) and small RNA sequencing (sRNA-seq) data analysis, inverse expression pattern analysis, publ

159 Supervised data analysis involved the use of 3 different machine le

160 We demonstrate that multiomics data analysis is a powerful approach to unravel the func

161 However, data analysis is often rate-limiting in high-throughput

162 Each data analysis job can be shared or cloned to disseminate

163 py combined with a quantitative unsupervised data analysis methodology developed in-house to visualiz

164 Standardized EDA data analysis methods are readily available.

165 rations of Gly were obtained, and a range of data analysis methods compared, leading to a detection l

166 c advancement of experimental techniques and data analysis methods that has made it possible to also

167 h includes an extensive set of commonly used data analysis methods that have been implemented using s

168 ) data is foundational for benchmarking Hi-C data analysis methods.

169 timated efficacy results from a longitudinal data analysis model.

170 isciplines rely on computational methods for data analysis, model generation, and prediction.

171 In biobank data analysis, most binary phenotypes have unbalanced ca

172 ies are critical to support the types of big data analysis necessary for kidney precision medicine, w

173 ication, the UK Biobank whole-exome sequence data analysis of 45,596 unrelated European samples and 7

174 This is a secondary data analysis of a cross-sectional survey of adult burn

175 We conducted a secondary data analysis of a large asthma case-control study invol

176 This is a secondary data analysis of a randomized clinical trial.

177 method development, software prototyping and data analysis of biobank scale sequence datasets in R.

178 atient blood samples, by taking advantage of data analysis of bright field microscopy images.

179 Retrospective data analysis of eyes previously diagnosed with neovascu

180 provides a foundation for standardizing ESI data analysis of larger molecules and enabling the use o

181 Secondary data analysis of randomized controlled trials published

182 We performed a secondary data analysis of the Taiwan National Health Insurance Re

183 ents may die before recovery or improvement, data analysis of this end point faces a competing risk p

184 Therefore, we performed a retrospective data analysis of two extensive HRMS campaigns each cover

185 BCR) is benefit for both quality control and data analysis of WGBS.

186 y complex causal structure of high-dimension data, analysis of high-dimension mediation currently req

187 ulations and discussions, we explore several data analysis options and the optimum design that balanc

188 ting trials, either through individual-level data analysis or systems science modeling (Am J Epidemio

189 proteomic data and recommends instrument and data analysis parameters for improved data quality.

190 owever, DHDNs face a number of challenges in data analysis, particularly in the presence of missing d

191 ion allows the freedom to interface with the data analysis pipeline while maintaining a user-friendly

192 Those genes will likely be missed by any data analysis pipeline, such as enrichment analysis, tha

193 ogists by allowing for a flexible, expansive data analysis pipeline.

194 brary construction strategies coupled to new data analysis pipelines allowed the mapping of specific

195 The interface utilizes hardware elements and data analysis pipelines already established for DESI-MS

196 olution, pyphe, for automating and improving data analysis pipelines associated with large-scale fitn

197 There are a variety of well-established data analysis pipelines available, including mothur and

198 investigation of 278 representative RNA-seq data analysis pipelines consisting of 13 sequence mappin

199 pectrometry have created a need for improved data analysis pipelines for deconvolution of electrospra

200 Many NGS HIVDR data analysis pipelines have been independently develope

201 ly integrated into existing high-performance data analysis pipelines or as a Python package to implem

202 ng the use of ESI deconvolution in automated data analysis pipelines.

203 The key technology and data analysis platforms necessary to implement systems g

204 We recommend a careful exploratory data analysis prior to application of any inferential mo

205 ry.org, can ground hypothesis generation and data-analysis priors on a comprehensive view of publishe

206 Current data analysis procedures, however, either fail to exploi

207 ndling and software for data acquisition and data analysis, process control, audit trails and automat

208 Furthermore, the preprocessing and the data analysis processing steps, including calibration an

209 study emphasizes the need for a standardized data analysis protocol for qPCR MST assays for interlabo

210 Small RNA-Seq (sRNA-Seq) data analysis proved to be challenging due to non-unique

211 ex method and G-statistic method for BSA-Seq data analysis require relatively high sequencing coverag

212 alleviates the burden of manually executing data analysis required to test biologically meaningful h

213 n for 80 worms takes 3-4 d, while the entire data analysis requires 10-30 min.

214 Our data analysis revealed that multiple biological processe

215 Clinical data analysis reveals that ASB13 expression is positivel

216 at allows researchers to publish and execute data analysis scripts.

217 Simulation studies and real data analysis show that DASEV substantially improves par

218 Postmortem DLPFC expression data analysis showed decreased expression levels of NURR

219 Sensory data analysis showed that after addition of PHs to apple

220 d spectroscopy (FTIR) imaging with automated data analysis showed that polyamide (39%) and ethylene-p

221 Longitudinal data analysis showed that the psychiatric problems, espe

222 NA and a use case inspired by RNA sequencing data analysis, showing how PyGNA can be easily integrate

223 Furthermore, TCGA data analysis shows an inverse correlation between Arf1

224 RNA-sequencing data analysis shows that Lbs are expressed as early as a

225 To date, no automated open source PFAS data analysis software exists to mine these extensive da

226 research team and entered into a qualitative data analysis software.

227 hen the sample preparation methodologies and data analysis strategies that address these constraints

228 setups, fluorescence detection schemes, and data analysis strategies that enable the study of struct

229 o compare the performance of eight different data analysis strategies.

230 The success of a multi-omic data analysis strategy depends largely on the adoption o

231 sample preparation and tailored statistical data analysis, substantially improves RIC's quantitative

232 remain in data-independent acquisition (DIA) data analysis, such as to confidently identify peptides,

233 r embeddings in a wide variety of downstream data analysis tasks, such as visualization, clustering,

234 We propose a new method based on Topological Data Analysis (TDA) consisting of Topological Image Modi

235 Using topological data analysis (TDA), we present a classifier for repeate

236 Here, we use a multivariate data analysis technique to estimate variability in barys

237 Using random forests as an IMS data analysis technique, it was possible to identify the

238 gene expression data is one such popular bio-data analysis technique.

239 Here, we survey data analysis technologies that facilitate the effective

240 scipline requiring innovative approaches for data analysis that can combine traditional and data-driv

241 t is a frequent challenge in deep sequencing data analysis that can lead to misleading conclusions.

242 e terroir concept, we set up a new method of data analysis that inputs heterogeneous data from analyt

243 based GUI, particularly advanced dimensional data analysis that required much explanation.

244 ine Learning (ML) is a powerful tool for big data analysis that shows substantial potential in the fi

245 Thus, SSIM is a general strategy for FSCV data analysis that uses three-dimensional data to detect

246 In time-to-event data analysis, the Cox proportional hazards (PH) regress

247 From a real data analysis, the most significant eQTL discoveries dif

248 Because of their powerful capabilities in data analysis, these virtual algorithms are expected to

249 where the data are combined in an integrated data analysis to appropriately assess the treatment effe

250 in an automatic manner, we apply topological data analysis to experimental data and to results of our

251 Proper normalization is required prior to data analysis to gain meaningful insights.

252 We use topological data analysis to leverage this observation and uncover 3

253 We apply tools from functional data analysis to model cumulative trajectories of COVID-

254 of compound design, synthesis, testing, and data analysis to provide new chemical probes and lead co

255 positional residue depletion/enrichment, and data analysis to suppress false-positive sequences from

256 iz as an integrative and interactive genomic data analysis tool that incorporates visualization tight

257 eed for timely computation in the short-read data analysis toolchain.

258 Most existing NGS data analysis tools focus on the microRNA component and

259 ata obtained from high-throughput sequencing data analysis tools like MISO, rMATS, Piranha, PIPE-CLIP

260 ated pathogen sequence databases, and better data analysis tools, there remain many challenges to the

261 were analyzed using UHPLC-HRMS and advanced data analysis tools.

262 Web-based CausalMGM consists of three data analysis tools: (i) feature selection and clusterin

263 st powerful labelling, clearing, imaging and data-analysis tools, scientists are extracting structura

264 Data analysis used the generalized linear mixed models R

265 iterature review, retrospective quantitative data analysis using Demographic and Health Surveys from

266 Topological data analysis using human epithelial transcriptomic data

267 Data analysis using hypothesis-driven population genetic

268 is study characterized the effects of a qPCR data analysis using the sample PCR efficiencies (the Lin

269 Notably, patient tumor data analysis validates these findings by revealing that

270 This secondary data analysis was conducted by combining 4 different coh

271 Qualitative data analysis was conducted using meta-ethnography, foll

272 Data analysis was done using SPSS version 22.0 software

273 Data analysis was done using Statistical Package for Soc

274 Data analysis was facilitated by determining the fragmen

275 Data analysis was limited to adult intestinal transplant

276 Data analysis was performed using generalized logistic a

277 Data analysis was performed using statistical software S

278 A secondary data analysis was performed with data collected from 2 s

279 However, the data analysis was still made with the correct immunoblot

280 Multivariate data analysis was used to distinguish patients with TB f

281 lecting, Thinking (NCT) model of qualitative data analysis was used to identify key themes.

282 Topological data analysis was used to visualize the data set and clu

283 sample preparation, peptide separation, and data analysis, we aimed to uncover the full potential of

284 Using both simulation studies and real data analysis, we demonstrate that even though CoMM-S2 u

285 order to reduce the complexity of scRNA-seq data analysis, we present scedar, a scalable Python pack

286 In this population-based, panel data analysis, we used data from three waves of the nati

287 distribution, bootstrapping, and topological data analysis were applied.

288 pproaches and provide practical guidance for data analysis when using these methods.

289 olution is an indispensable step in spectral data analysis, which groups spectral peaks into isotopic

290 of instrumentation, sample pretreatment, and data analysis will help realize their translation to cli

291 en demonstrate the utility TL for integrated data analysis with an example for spatial single-cell an

292 m MS analysis of the digested sample and the data analysis with Protein Metrics Suite.

293 has been examined using whole transcriptome data analysis with RNA-seq.

294 We have developed PVAmpliconFinder, a data analysis workflow designed to rapidly identify and

295 The data analysis workflow presented implements an approach

296 A data analysis workflow revealed that distinctive spectra

297 cell fates utilizing a newly developed image-data analysis workflow.

298 latform bioinformaticians are able to deploy data analysis workflows (recipes) that their collaborato

299 ange of R/Bioconductor packages into 'omics' data analysis workflows represents a significant challen

300 This toolbox can be used to build data-analysis workflows for metabolomics and other omics