ライフサイエンスコーパス: data processing

1 data collection to automated recognition and data processing.

2 address a fraction of the steps required for data processing.

3 several key advantages to traditional LC-MS data processing.

4 sting infrastructure for data management and data processing.

5 ished untargeted metabolomic method for XCMS data processing.

6 ion limit should become routine in FT-ICR-MS data processing.

7 fLM and cryo-ET images, which is followed by data processing.

8 out expensive instrumentation or complicated data processing.

9 use of IR spectra combined with multivariate data processing.

10 ess of the indexing step and the progress of data processing.

11 to-noise ratio, without the need for special data processing.

12 ew, or updated, software tools to facilitate data processing.

13 ht mass spectrometry (GC-TOF MS) and BinBase data processing.

14 e, leading to general guidelines for ethical data processing.

15 hematical algorithm was developed to aid the data processing.

16 o use and stable tool for rapid analysis and data processing.

17 /histopathological information and ambiguous data processing.

18 h for UPLC-MS analysis and 1-2 h for initial data processing.

19 ion, in complete analogy to filtering during data processing.

20 l packets leads to semi-analytic methods for data processing.

21 both smoothing-based and segmentation-based data processing.

22 house-written computer program for automated data processing.

23 ce protocol, circumventing the need for bulk data processing.

24 pt task sets that configure moment-to-moment data processing.

25 cDNA hybridization, experimental design, and data processing.

26 the basis of spike-in standards and uniform data processing.

27 proaches which require harmonic analysis for data processing.

28 ficantly improving the efficiency of cryo-EM data processing.

29 ity with probe control, data acquisition and data processing.

30 hallenging because of uncertainties in GRACE data processing.

31 instrumentation, acquisition parameters and data processing.

32 of storage and the efficiency of the initial data processing.

33 ensitivity, and standardized and transparent data processing.

34 an improved repeatability versus untargeted data processing (72.92% versus 62.12% of species display

35 A distributed implementation of the MHMM data processing accelerates data clustering efforts.

36 The new mass spectral data processing algorithm incorporates a multiple linear

37 trate the use of ultrasparse sampling, a new data processing algorithm, and a 4-D time-shared NOESY e

38 ra with no crosstalk using a postacquisition data processing algorithm.

39 Countless proteomics data processing algorithms have been proposed, yet few h

40 ts in Raman spectroscopy instrumentation and data processing algorithms have led to the emergence of

41 Fast data processing algorithms result in nearly delay-free b

42 ssing modules, such as MS storage systems or data processing algorithms.

43 -particle electron cryo-microscopy (cryo-EM) data processing allowing for the rapid determination of

44 enhanced features not available in other CD data processing/analysis packages.

45 s the CDtool software not only enables rapid data processing and analyses but also includes many enha

46 To our knowledge, software tools to automate data processing and analysis from sample fractionating (

47 review we discuss critical issues related to data processing and analysis in proteomics and describe

48 The data processing and analysis pipelines used the Perl pro

49 The computationally intensive data processing and analysis pipelines were run on an Ap

50 uid chromatography/mass spectrometry (LC/MS) data processing and analysis platform, MET-COFEA (METabo

51 Data processing and analysis requires a further 2 weeks,

52 exible workflow platform that can accelerate data processing and analysis so more time can be spent o

53 WGS data processing and analysis was done by staff masked to

54 ed detection, reagent kits, and software for data processing and analysis, is an automated method usi

55 there are significant challenges in terms of data processing and analysis, since neuronal signals hav

56 maticians/computer scientists/physicists for data processing and analysis.

57 atively little attention has been applied to data processing and analysis.

58 s make ProSeq3 a convenient hub for sequence data processing and analysis.

59 ularly useful for next-generation sequencing data processing and analysis.

60 nstrate how calls to existing R packages for data processing and calls to PathVisioRPC can directly w

61 c displays, biomedical imaging and ultrafast data processing and communication, might be overcome by

62 rized by a very simple architecture in which data processing and concentration retrieval are straight

63 lithographic processes, imaging techniques, data processing and data storage.

64 JS-MS enables custom MS data processing and evaluation by providing fast, 3-D vi

65 lexible scientific environment, facilitating data processing and general accessibility independent of

66 and could be easily integrated into other R data processing and graphic manipulation pipelines for p

67 tools have been developed or modified for MS data processing and interactive web display.

68 ge the results of different microbial genome data processing and interpretation stages, and represent

69 ition was designed to allow for simultaneous data processing and metabolite characterization.

70 nstrated that bias correction in preliminary data processing and optimal statistical testing signific

71 nst XCMS Online, the widely used cloud-based data processing and pathway analysis platform.

72 e for data collection and avoided subsequent data processing and peak-picking.

73 and promoter dynamics, integrated with CAGE data processing and promoterome mining into a first comp

74 data import, quality control, visualization, data processing and quantitation.

75 advance to facilitate untargeted metabolomic data processing and quantitative analysis and their gene

76 ssing natural media as platforms for optical data processing and quantum information applications.

77 In this work, a novel algorithm for data processing and rigid-body motion correction (MC) fo

78 A workflow including XCMS Online for data processing and robust confirmatory statistics was u

79 tum computation, or the spin-operation-based data processing and sensing.

80 fers a collection of tools that automate the data processing and simplify the computationally intensi

81 processing) so that computationally intense data processing and statistical analyses can run on a re

82 igations may lead to applications in optical data processing and storage or the realization of quantu

83 lectronics are extremely efficient in sensor data processing and transmission.

84 Technical considerations of data processing and use of the ToxCast database are pres

85 be made available to all; previously manual data-processing and analysis tasks can be automated by h

86 inting assay, (4) analysis with HR-MSMS, (5) data processing, and (6) selection of biomarkers.

87 luding target preparation, hybridization and data processing, and data analysis.

88 n emphasis on information theory, sequential data processing, and optimality arguments.

89 pplications, such as microscopy, all-optical data processing, and quantum information.

90 es an iterative process between experiments, data processing, and theoretical analysis.

91 (QC), molecular identification using MS/MS, data processing, and visualization with 3D models of the

92 nd data sources, including global food trade data, processing, and packaging models.

93 IDEOM provides a user-friendly data processing application that automates filtering and

94 harvesting, detection, sensing and photonic data processing applications.

95 xity exceeds the capabilities of traditional data processing applications.

96 SuperQuant is a quantitative proteomics data processing approach that uses complementary fragmen

97 ecaose (bracketing the peaks of interest), a data processing approach was designed and developed to s

98 When choosing a data processing approach, the frequency and phase conten

99 ometer to planktic foraminifera with a novel data-processing approach.

100 ric data were explored with novel untargeted data processing approaches (enviMass, nontarget, and RMa

101 ts the performance assessment of alternative data processing approaches and of alternative experiment

102 These data processing approaches increase the amount of inform

103 xplicitly considered, as were the effects of data processing approaches that may limit the impact of

104 ples, integrated computational workflows for data processing are needed.

105 h is suitable for terahertz rate all-optical data processing as well as ultrafast optical limiters an

106 LC/MS provides linearity in response, data processing automation, improved limits of detection

107 lags were determined and corrected using VBA data processing based on the synchronization of the isot

108 Prior to data processing, baseline correction and retention time

109 ansfer times still represent a bottleneck in data processing because of the increasingly complex data

110 ing at kHz rates is combined, with real-time data processing being accelerated by a graphics-processi

111 ing of pre-symptomatic SOD1 mouse models and data processing by a correlation-based algorithm reveale

112 /MS assays take from 4 h to several days and data processing can be done in 1-7 d.

113 urve is enhanced and the total time used for data processing can be reduced.

114 developments in fast electron detectors and data processing capability is shown to enable electron p

115 leagues reflected nonstandard experiment and data processing choices, and selective scoring rules.

116 The development of metamaterials, data processing circuits and sensors for the visible and

117 tion carriers for highly integrated photonic data processing circuits.

118 After acquisition, data processing consists of a sequence of steps includin

119 by Bio-Plex Manager software; (ii) customize data processing, curve fits, and algorithms through scri

120 To facilitate the analysis and data processing, customized and centralized databases ar

121 esign, sample preparation, data acquisition, data processing, data analysis and interpretation relati

122 ges related to data-driven approaches (e.g., data processing, data availability, data quality, data c

123 digestion, labeling, liquid chromatography, data processing, database searching and statistical anal

124 ential technology for data storage and other data processing devices.

125 With suitable acquisition and data processing, each diastereomer exhibits characterist

126 ded in manufacturers' instrument control and data processing environments.

127 Although different practices of data processing exist, in this case they do not substant

128 demonstrate that this method is reliable for data processing, five (13)C2-/(12)C2-dansyl labeled meta

129 dance spectroscopy was employed in impedance data processing, followed by calibration with the electr

130 tomatically combined targeted and untargeted data processing, followed by verification and quantitati

131 Data processing for 1D NMR spectra is a key bottleneck f

132 onditions, and the application of a tailored data processing for handling of plasma effects and high

133 ignment, imaging operation, laser safety and data processing for in vivo fPAM.

134 e CRP level in test samples was generated by data processing from the intensities of three lines.

135 NaviSE allows different entry levels of data processing, from sra-fastq files to bed files; and

136 nces in physics, chemistry, engineering, and data processing have enabled significant increases in se

137 tissue volumes, but advances in neuroimaging data processing have made it possible to separate cortic

138 h of physics which promises to revolutionize data processing, improve photovoltaics, and increase sen

139 mple pretreatment and improved detection and data processing, including chemometric tools.

140 elf-consistency condition closely related to Data Processing Inequality.

141 To explain it, we hypothesize that data processing is a critical element that impacts the d

142 The final output of the automated data processing is a set (or the average) of the most po

143 d with optimized univariate and multivariate data processing is a sufficient tool to distinguish betw

144 HDX data processing is achieved with a comprehensive HDX mod

145 tographic and spectral peak finding, initial data processing is based on accurate mass-matching to fu

146 However, GCxGC/TOF-MS data processing is currently limited to vendor software

147 mproved HDX MS platform with fully automated data processing is described.

148 he noise of the simulated taylorgrams on the data processing is discussed.

149 A strategy for optimizing LC-MS metabolomics data processing is proposed.

150 is of different groups of samples, automated data processing is required.

151 However, the speed of this data processing is such that it is usually performed off

152 ning relevant information about the samples, data processing is usually the bottleneck because of the

153 In order to overcome these data processing issues, we introduce MetaCRAM, the first

154 genomic experiments represent a challenge in data processing, management, and analysis.

155 Herein, we report a data processing method based on the use of a mass spectr

156 act ion-selective electrodes (SC-ISEs) and a data processing method for sensor calibration and drift

157 gularized Linear Inversion approach as a new data processing method to extract the probability densit

158 A suitable data processing method was created that should be suffic

159 Data processing methods and denoising algorithms have be

160 Standardized data processing methods are proposed for consistent data

161 Moreover, results showed some data processing methods can skew sequence-based biodiver

162 Various data processing methods have been developed for explorin

163 spectra and the impact of pulse sequence and data processing methods on the sensitivity of pattern re

164 n must be based on unbiased, high throughput data processing methods to identify relevant biological

165 preparations, chromatography conditions, and data processing methods were kept identical.

166 authentication was evaluated using different data processing methods, leave-10%-out cross-validation,

167 rely limited, and its selection is biased by data processing methods.

168 action images, in part due to limitations of data processing methods.

169 We also discuss data-processing methods for confocal microscopy and comp

170 cy analysis for determination of the optimal data-processing methods.

171 h for MSI data analysis, combining automated data processing, modeling and display, is user-friendly

172 encies and are not well suited for custom MS data processing modules, such as MS storage systems or d

173 However, suboptimal study design and data processing negatively affect CNV assessment.

174 Extensive variations of data processing, normalization, and modeling parameters

175 ems for conditions that data-transmitting or data-processing occurs with a non-negative entropy gain.

176 tress has been elusive due to limitations in data processing of current techniques.

177 toolset that assists in quality control and data processing of high-throughput RNA sequencing data.

178 The PV-OCT images were generated by software data processing of the entire cross-sectional image from

179 ) analysis by initiating data conversion and data processing on subsets of data acquired, expanding i

180 uantification, efficient post-quantification data processing, optimized pooling and fragmentation, an

181 hout the need for optical instruments or any data processing or plotting steps.

182 ire experimental workflows (from sampling to data processing) or different analytical platforms in th

183 out without any assistance from instruments, data processing, or graphic plotting.

184 chemistry has been limited by bottlenecks in data processing, particularly noise filtering and metabo

185 We consider models as the primary tool for data processing, pattern identification, and scenario an

186 ides a benchmark set for both laboratory and data processing performance assessments.

187 package is one of the key components of the data processing pipeline and implements automated algori

188 AN and can be incorporated in a metabolomics data processing pipeline facilitating large screening as

189 ave been added to RefSeq prokaryotic genomes data processing pipeline including the calculation of ge

190 gies were discussed accordingly, and a novel data processing pipeline was proposed that combines seve

191 neTrack automates several steps of a typical data processing pipeline, including smoothing and peak d

192 used as a module in a complete metabolomics data processing pipeline.

193 G's microbial genome and metagenome sequence data processing pipelines and are integrated into the da

194 ce for easy integration into high-throughput data processing pipelines.

195 isons among studies performed using distinct data processing pipelines.

196 ques, mass spectrometry instrumentation, and data processing platforms continue to spur growth in the

197 t can be used to tackle the hugely demanding data-processing problems encountered in the natural scie

198 presents application of sequential enhanced data processing procedures to high-resolution tandem mas

199 Running Circos requires extra data processing procedures to prepare plot data files an

200 1481 chromatograms produced by two different data processing programs used in-house (MZmine2 and late

201 An advanced data processing protocol was established to overcome flu

202 l be carried out as part of a single unified data-processing/quality control run, greatly reducing bo

203 iate attention to instrument performance and data processing, quantitative protein assays with good s

204 de the capabilities for remote monitoring of data processing, real time notifications for the data pr

205 alysis and 2-3 h for preliminary/exploratory data processing, representing a robust method for untarg

206 g mechanisms, sensor fabrication, power, and data processing requirements.

207 novel, alternative solutions in all-optical data processing research.

208 zzle-skimmer dissociation (NSD), and aligned data processing resources to rapidly characterize abunda

209 t shallow depth (<5 km) constrained by InSAR data processing results from early post-seismic deformat

210 It is furthermore demonstrated that targeted data processing results in an improved repeatability ver

211 protocol also includes data acquisition and data processing routines customized for chemical exchang

212 With advanced microfabrication and data processing, SBCR will become more compact, lighter,

213 nsembl computing infrastructure with a novel data processing scheme.

214 s and stream recently acquired data files to data processing servers, mimicking just-in-time producti

215 ences between the three sampling approaches, data processing showed that the three methods provide th

216 These findings demonstrate that data processing significantly influences data quality, w

217 line elimination of the dissolved metal made data processing simpler and more accurate.

218 llumina and Affymetrix is a critical step in data processing, so that accurate information on genetic

219 , recommended procedural guidelines, and new data processing software (LIMS for Lasers) that altogeth

220 roposed strategy can be applied to any other data processing software involving parameters to be tune

221 rs are used by the Chameleon work-flow based data processing software to generate absorption mode "Da

222 Previous containerized data processing solutions were limited to single user en

223 At the same time, the data-processing speeds required by today's technology ne

224 ug/L standard mix was tracked throughout the data processing stages, where 406 targets were successfu

225 Data processing, statistical analysis and metabolite ann

226 he critical difficulties that appear at each data processing step and that can dramatically affect th

227 However, they employed an erroneous data-processing step, overestimating Pol II differences.

228 data as input and carries out a sequence of data processing steps including construction of extracte

229 arrier of entry for biologists by automating data processing steps needed for knowledge extraction fr

230 Traditionally data processing steps such as noise removal, background

231 ferent instruments, as well as the effect of data processing steps.

232 Here we have developed a sequence of data-processing steps to retrieve background-free and no

233 thods, peptide identification algorithms and data-processing steps, the analysis of deuterium levels

234 The developed data processing strategies may be transferred to other r

235 and have enabled increasingly sophisticated data processing strategies, indicating a bright future f

236 In this study, we introduce a novel LC-HRMS data processing strategy for the reliable classification

237 This work proposes a data processing strategy to overcome this instrumental l

238 ormalization methods, and propose a rational data processing strategy, for robust evaluation and mode

239 Its data processing structure enables rapid image display an

240 Ca2+-imaging experiment, accomplish offline data processing (such as background correction) and conv

241 hosilicate PET/CT scanner with a new digital data processing system (Pico-3D).

242 We implemented a data processing system based on classical post-refinemen

243 oftware implements common mass spectrometric data processing tasks through a well-defined application

244 made workflows for common mass spectrometric data processing tasks, which enable users to perform com

245 h automatically parallelizes and distributes data processing tasks.

246 command line interface for high-performance data processing tasks.

247 ate, there has been little attention to this data-processing technique in metabolomics.

248 g a plasmonic sensor array incorporating the data-processing technique termed "algorithmic spectromet

249 ary asteroid survey instruments and improved data processing techniques are likely to result in the d

250 Current data processing techniques either require control sample

251 Now, deploying specialized data processing techniques to achieve super-resolution i

252 Various data processing techniques were exploited to reduce the

253 inherent differences in methodologies used, data processing techniques, and ascertainment bias.

254 roved accuracy and stability, along with new data processing techniques, has improved the quality and

255 er, require consistent, sensitive and robust data-processing techniques for successful biomedical app

256 Data processing templates can be generated and saved for

257 ted by low sample throughput and complicated data processing that contribute to false discoveries.

258 sing SHAPE is the complex and time-consuming data processing that is required.

259 are tool for magnetic resonance spectroscopy data processing that is widely used in the magnetic reso

260 Without additional data processing, the overall median absolute relative di

261 Following data processing, the signals are converted into concentr

262 ess one of the essential steps in proteomics data processing--the matching of peptide measurements ac

263 ripts and achieves a near linear decrease in data processing time as a result of increased multi-thre

264 With total data processing times below 1 s, the OC-LIBS procedure a

265 the need for a time zero spectrum as well as data processing to account for natural abundance heavy i

266 speed fluorescence scanning, and large-scale data processing to arrive at statistically significant c

267 sample handling capabilities, and automated data processing to improve throughput.

268 conclusion, FAST allows for high-throughput data processing to match the current high-throughput gen

269 nly (before HDX or "time zero") spectrum and data processing to remove its contribution.

270 the precursor selection gives access, after data processing, to the same structural information cont

271 management systems to perform bi-directional data processing-to-visualizations with declarative query

272 An automated data processing tool, FlavonQ, was developed that can tr

273 pite the ubiquity of mass spectrometry (MS), data processing tools can be surprisingly limited.

274 d or fourth decimal place; however, existing data processing tools do not capitalize on this informat

275 or this is a lack of suitable and accessible data processing tools for the analysis of large arrayed

276 opment of richer mass spectral libraries and data processing tools have enabled large scale metabolic

277 Many popular data processing tools, including XCMS-online and MZmine2

278 olution mass spectrometry and using advanced data processing tools, we demonstrate much extended cove

279 tatistical variation created by selection of data processing tools.

280 a detailed user's guide and an assortment of data processing tools.

281 replaces the functionality of numerous other data-processing tools, and can quickly and efficiently g

282 de facto standard framework for distributed data processing using the MapReduce formalism.

283 The data processing using TXRF-XANES spectra of U(IV), U(V),

284 open up the exciting possibility of digital data processing utilizing antiferromagnetic spin waves a

285 processing, real time notifications for the data processing, visualization and interactive analysis

286 System control and data processing was by MassLynx 4.0 with QuanLynx Applic

287 A Visual Basic for Applications (VBA) data processing was developed to count and sort the part

288 The data processing was tested in terms of the calculated GU

289 mated data-dependent analysis and subsequent data processing, we couple the technique with an online

290 field of metabolomics and heavy workload of data processing, we designed the first remote metabolomi

291 intains efficient compression and downstream data processing, while allowing for unprecedented levels

292 Untargeted data processing with DIA-Umpire provided a means of iden

293 prepare samples, and to control hardware and data processing with our software.

294 HDCV analysis streamlines data processing with superior filtering options, seamles

295 tionships between instrument performance and data processing with the aim of determining whether this

296 The method enables data processing with the crystallographic software tool

297 lean-up, GCxGC-ToFMS detection and automated data processing with the non-proprietary free downloadab

298 r or independently to build applications and data processing workflows relevant to drug discovery and

299 e for integrating modeling with experimental data processing workflows, facilitated by a comprehensiv

300 ingly accurate and efficient high-throughput data processing workflows.