ライフサイエンスコーパス: raw data

1 in the model ("direct boundary model" of the raw data).

2 ect application of SVD or normalized cuts to raw data).

3 methodology would be applied to the obtained raw data.

4 that imaging time we reframed the list-mode raw data.

5 tive statistical method from the same set of raw data.

6 EPA were for raw data.

7 viding a useful visual summary of underlying raw data.

8 ly two IQA requests to the U.S. EPA were for raw data.

9 ll as its ability to easily process ChIA-PET raw data.

10 asured using accelerometry after reanalyzing raw data.

11 only used summaries of the profiles based on raw data.

12 be achieved by postacquisition processing of raw data.

13 ngth and step frequency were determined from raw data.

14 he temporal order may not be apparent in the raw data.

15 and offering a simple way of visualizing the raw data.

16 rdant, even though the methods used the same raw data.

17 ta may miss spatial artefacts present in the raw data.

18 quality assessment to be carried out on the raw data.

19 dentifying the physical imperfections in the raw data.

20 methods used to obtain mass values from the raw data.

21 stograms (method 2) were calculated from the raw data.

22 in the mean are estimated automatically for raw data.

23 ics have produced an unprecedented amount of raw data.

24 again with substantial improvement over the raw data.

25 locations are successfully recovered from RH raw data.

26 dies and a mean peak area RSD of <15% in the raw data.

27 and promise to flood current databases with raw data.

28 eling experiments from LC-high-resolution MS raw data.

29 hat likely caused deleterious effects on the raw data.

30 and 10% more proteins quantified on the same raw data.

31 t be selected from an overwhelming amount of raw data.

32 of a hundred smaller than using gzip on the raw data.

33 d samples by generating MD5 fingerprints for raw data.

34 The methodology, which is based on raw data acquisition followed by image processing, is he

35 comes of single analyses; however, comparing raw data across multiple experiments should enhance both

36 all stages of DNA sequencing data analysis: raw data, alignment, and variant detection.

37 The raw data allow base calling up to 460 bp with an accurac

38 Access to the raw data allows for a more detailed quality assessment a

39 icles containing at least 1 scatterplot with raw data and a corresponding fitted regression line were

40 es' upper thermal tolerance limits, both for raw data and after accounting for the effects of phyloge

41 Two authors independently abstracted raw data and assessed methodological quality.

42 tron sliding." Matches were tallied from the raw data and compared with the expected number of matche

43 k by the absence of sustainable archives for raw data and derivative visualizations.

44 Outcomes were presented as raw data and descriptive statistics (means +/- standard

45 solGS enables breeders to store raw data and estimate GEBVs of individuals online, in an

46 This pattern held for raw data and for phylogenetically independent contrasts.

47 rch, it should provide sufficient underlying raw data and information about methods to enable reanaly

48 rch, it should provide sufficient underlying raw data and information about methods to enable reanaly

49 reflecting the artefact-prone nature of the raw data and lack of standards for dealing with the arte

50 f the pipeline used to generate high quality raw data and mitigate the need for batch correction are

51 We have scrutinized Stern's raw data and observe that his automated song pulse-detec

52 iscuss recent developments in ways of seeing raw data and presenting the results of statistical model

53 ntific community, including public access to raw data and protocols, the conduct of replication studi

54 We analyzed raw data and reconstructed images, including no correcti

55 lemented in the tool to: (1) read and import raw data and spectral libraries; (2) perform GC-SIM-MS d

56 using an open science approach, sharing both raw data and stimuli.

57 based on an intent-to-treat approach, using raw data and the blood pressure categories of prehyperte

58 because NP databases are not searchable with raw data and the NP community has no way to share data o

59 Careful examination of the raw data and the use of masses for predicted metabolites

60 oefficient values (CCV) of the mass spectral raw data and their variation was developed and used to a

61 r significant retention time shifting in the raw data and then demonstrate subsequent corrections of

62 pectratype analyzers to SpA, which saves the raw data and user-defined supplementary covariates to a

63 hesis of findings, increased availability of raw data, and a focus on good study design, all of which

64 as between normal and cancer cells; download raw data; and generate heatmaps; and finally, use its in

65 Programs, parameter sets and raw data are available online at.

66 Cross species comparative mapping raw data are collected and the processed information is

67 ult of the mathematical process by which the raw data are converted into Kubelka-Munk units, and we d

68 Raw data are corrected using a calibration based on five

69 Raw data are deposited on SRA, accession numbers: brain

70 by the age of the implant, although when the raw data are examined, some trends are seen.

71 Because hundreds of gigabytes (GB) of raw data are generated from a GWAS, the samples are typi

72 goal: Biological annotations associated with raw data are often not normalized, and the data themselv

73 es to extracting spike times and labels from raw data are time consuming, lack standardization, and i

74 d searches; all source references, including raw data, are clearly described and hyperlinked.

75 ntegrating the statistical properties of the raw data as well as information of dense objects gained

76 of searches for arbitrary k-mers within the raw data as well as the ability to reconstitute arbitrar

77 approximation of Pdo may be derived from the raw data, as an alternative to exponential curve fitting

78 sult from the investigators' analyses of the raw data, as implemented in Lists of Lists Annotated (LO

79 These values represent the quality of the raw data, as no normalization or feature-specific intens

80 d not obviate rights under the IQA to obtain raw data at a later point.

81 roximately 13,000 comments were added to the raw data at the time of collection.

82 ighlight limitations inherent with a lack of raw data availability, insufficient clinical/histopathol

83 ible than a system that attempts to make all raw data available proactively.

84 Raw data-based iterative reconstruction yielded equivale

85 GC(2)MS also directly corrects the raw data baseline.

86 ld similarly have to include a balance among raw data, basic feature detection results, sufficiency i

87 spectroscopic data involves the denoising of raw data before any further processing.

88 structure fits two sets of crystallographic raw data best.

89 ed databases such as TrEMBL or GenPept cover raw data but provide only limited annotation.

90 An analysis pipeline transforms raw data by performing simple analyses (i.e., vector rem

91 the biological system is extracted from the raw data by statistical methods such as used in fluctuat

92 Artificial image noise was added to the CT raw data by using a dedicated software platform.

93 The Ag Spike raw data can be accessed at http://www.ccr.buffalo.edu/h

94 The exponential component of the raw data can be extracted and defined as the "extracted

95 thod, we have shown that the high-resolution raw data can be fully utilized without applying any arbi

96 Files and raw data can be imported and associated with the biologi

97 le use unless the resulting large volumes of raw data can be reliably translated into actual behaviou

98 monstrate that variances calculated from the raw data can be used as inverse weights in the DE analys

99 Errors in the raw data can lead to insertion or deletion errors (indel

100 spike times of the recorded neurons from the raw data captured from the probes.

101 ilitate computational analyses, M3D provides raw data (CEL file) and normalized data downloads of eac

102 Data deposition: GeneChip raw data (CEL-files) have been deposited for public acce

103 from the level of physical manipulation and raw data collection to automated recognition and data pr

104 Depending on the PET raw-data compression, the average correlation between MR

105 pected from whole-organism DNA sampling, our raw data contained reads from nontarget genomes.

106 The raw data, curated annotation, and code used to create ou

107 s a suite of bioinformatics tools to perform raw data deconvolution, metabolite putative assignment,

108 y provided a full protocol and none made all raw data directly available.

109 samples while keeping all interpretation of raw data directly in the hands of the analyst-saving gre

110 nology-specific biases and produces distinct raw-data distributions, researchers have experienced dif

111 atical approach was developed and applied to raw data exported after the chromatographic course, in o

112 Statistical analysis was performed on raw data extracted from the TIC analysis.

113 ading necessary materials for converting the raw data files (*.CEL) for comparative analysis.

114 rocessing software that captures whole-brain raw data files as they are being produced from the MR un

115 The raw data files for the samples were assembled into a sin

116 ML and mzData it can be difficult to extract raw data files in a form suitable for batch processing a

117 these parameters are embedded in instrument raw data files, an opportunity exists to capture this me

118 apidly extracting semiquantitative data from raw data files, which allows for more rapid biological i

119 tem or directly from an MRI scanner, or from raw data files.

120 perimental protocols, related literature and raw data files.

121 high signal-to-noise ratio by coaddition of raw data, flexible excitation, reduced complexity of ele

122 ibuted workflow processes and open access to raw data for analysis by numerous laboratories.

123 The 2039 alleles identified served as raw data for estimating genetic structure and diversity.

124 nterfaces, both for download of all types of raw data for independent analysis, and also for straight

125 e incidence of CVE post-TAVR while providing raw data for predictors of interest was performed.

126 tion for meta-analyses and, if possible, the raw data for re-analyses.

127 ion leads to additional information, such as raw data for specific isotopic forms or for metabolites

128 PM implantation after TAVR and that provided raw data for the predictors of interest.

129 In fact, the raw data for the standard curves were highly scattered a

130 This package also contains the raw data for the three example datasets used in this man

131 uitment into the study, and (e) inclusion of raw data (for true-positive, false-positive, true-negati

132 es, Z- and time stacks in a broad variety of raw-data formats, as well as movies and animations.

133 e obtain high-quality images of objects from raw data formed from an average of fewer than one detect

134 G, AND PATIENTS: Post-hoc analysis combining raw data from 4 prospective randomized trials (performed

135 e report the first post-processing of binary raw data from a high-resolution LSCI camera.

136 We show that direct analysis of raw data from a quantitative genotyping platform can det

137 , we present our experience of analysing the raw data from an Illumina spike-in experiment and offer

138 tic and is capable of post-processing binary raw data from any camera source to improve the sensitivi

139 Analysis of raw data from assays carried out on the diffractive micr

140 Raw data from emission scanners contained in ECT sinogra

141 The R/Bioconductor package beadarray allows raw data from Illumina experiments to be read and stored

142 QuaMeter can directly read raw data from instruments manufactured by different vend

143 strategy, in which statistical treatment of raw data from liquid chromatography-mass spectrometry (L

144 The Sequence Read Archive (SRA) contains raw data from many different types of sequence projects.

145 The raw data from many diverse proteomic experiments are mad

146 logical roles, locations, concentrations and raw data from metabolic experiments.

147 an order of magnitude better than published raw data from other instruments so that high-quality res

148 RNASEQR analyzes raw data from RNA-seq experiments effectively and output

149 There is increased demand for disclosure of raw data from studies used by the U.S. EPA in these revi

150 The raw data from such experiments are two-dimensional, but

151 PATIENTS AND METHODS Raw data from the Prostate Cancer Prevention Trial were

152 each phenotype, and also reprocessed all the raw data from the studies using a unified pre-processing

153 Using raw data from these 40 images, and a simplified model of

154 Using the raw data from these 40 images, and a simplified model of

155 A combined analysis of raw data from these studies was performed to better asse

156 rmed by mapping the predicted sequences with raw data from total transcript sequence generated using

157 The paper presents the raw data from which our conclusions were drawn and discu

158 cipant data (IPD) meta-analyses that obtain "raw" data from studies rather than summary data typicall

159 RawConverter accepts RAW data generated by either data-dependent acquisition

160 ol, IsoMS, has been developed to process the raw data generated from one or multiple LC-MS runs by pe

161 measured not by the quantity and accuracy of raw data generated, but how rapidly they can be harnesse

162 modeling questions, hiding from the user the raw data generation and conversion steps.

163 The mass spectrometric raw data has been deposited in PRIDE (PXD003737).

164 The raw data have been deposited in the RHdb database.

165 Once the raw data have been preprocessed, running CellNet takes o

166 e in miRDeepFinder include pre-processing of raw data, identifying conserved miRNAs, mining and class

167 iterature and additional analyses as well as raw data if appropriate.

168 mathematical correction(s) to be made to the raw data if the best possible model is to be formed.

169 ponents of ICED include (i) normalization of raw data; (ii) assignment of weights to genes from both

170 Domains of expression from raw data images are spatially integrated into a set of s

171 ng down to approximately 20 nm resolution in raw data images as well as 15-19 nm diameter probing are

172 er are clearly improved in comparison to the raw data images for frequencies above 2000 cm(-1).

173 ramework for reproducible research, allowing raw data import, quality control, visualization, data pr

174 There is excellent reproducibility of raw data, improved resolution of large fragments, and co

175 underlying data, performing better than the raw data in revealing important biological insights.

176 sually via some 'clustering analysis' on the raw data in some abstract high dimensional space.

177 al methods and phenotypic results, including raw data in the form of images and streaming time-lapse

178 e conducted using the random effect model on raw data in the StatsDirect statistical program.

179 performance by at least 50% compared to the raw data; in most cases, it leads to a strategy very clo

180 of the actual pictures and difficulties with raw data interpretation.

181 h a user-friendly interface and (i) converts raw data into a format for visualization on a genome bro

182 In bioinformatics, we pre-process raw data into a format ready for answering medical and b

183 is required to process this large amount of raw data into a format that facilitates the development

184 ogy itself and the algorithm used to convert raw data into expression estimates.

185 ocedures have been proposed for transforming raw data into genotype calls.

186 putational methodologies used to convert the raw data into inferences at the DNA level, and details t

187 development of equations that translate the raw data into liters of body water or kilograms of fat-f

188 a critical problem is how to integrate these raw data into meaningful biological information.

189 M), which accepts and preprocesses PBM probe raw data into median-binding intensities of individual k

190 By converting the instrument raw data into mzXML format as its input data, MetSign pr

191 It allows individual researchers to condense raw data into spectral libraries, summarizing informatio

192 ormalization, binning, smoothing) to process raw data into visualizable tracks.

193 ly extract the relevant information from the raw data is a robust tool for evaluating the number and

194 equencing reads in color-space; that is, the raw data is a sequence of colors, where each color repre

195 Raw data is available at NCBI's SRA with accession numbe

196 The raw data is deposited to ProteomeXchange (PXD001907).

197 Storing raw data is infeasible because of its enormous size and

198 Storing raw data is infeasible because of its enormous size and

199 Raw data is processed and mapped to genomic coordinates

200 Raw data is stored in a generic database schema, Chado N

201 ess association for related phenotypes where raw data is unavailable or inappropriate for analysis us

202 k nonlinear retention time correction at the raw data level.

203 However, the raw data, microarray probe intensities, are heavily proc

204 in the model error when compared against the raw data model.

205 o analyzing these information-rich datasets, raw data must undergo several computational processing s

206 In untargeted proteomics and metabolomics, raw data obtained with an LC/MS instrument are processed

207 rofiles, we first performed a meta-analysis: raw data of 1539 microarrays and 705 NGS blood-borne miR

208 n the genotype calling algorithm BRLMM using raw data of 270 HapMap samples analyzed with the Affymet

209 with use of odds ratios calculated from the raw data of every trial.

210 To this end, we collected the raw data of publicly available immune-related scRNA-seq

211 o an associated problem: the large volume of raw data often makes it challenging to analyze and integ

212 We aim to use raw data on HM markers at different genomic loci to (1)

213 d and EMBASE databases for studies reporting raw data on new-onset LBBB post-TAVR and the need for PP

214 ce code (Perl) can also be adapted to handle raw data output from other image analysis applications.

215 Raw data (predicted contact lists and 3D models) and sou

216 several limitations, including nonoptimized raw data preprocessing, imprecise image coregistration,

217 Users interact with raw data primarily in the form of extracted ion chromato

218 The resource supports archiving of raw data, processed data and metadata which are indexed,

219 The amounts of raw data produced are prodigious, and many computational

220 , retrieve, display and analyze the complete raw data produced by several additional microarray platf

221 However, the quantity of raw data produced by this technology requires efficient

222 We make the images and raw data publicly available, providing an initial morpho

223 There has been heavy focus on performing raw data quality control.

224 AM OFDM) stream with sidelobe filtering, the raw data rate expedites from 17.2 to 18.4 Gbps.

225 lobe filtering are respectively delivered at raw data rates of 16.4 and 18 Gbps with spectral-density

226 or determining hyperfine-coupling signs; and Raw-DATA (RD)-PESTRE, a PESTRE variant that gives a cont

227 e automatic, including autocomparison of the raw data read by different technicians from the same gel

228 ion (+/- 10 to 15 per thousand), however the raw data required daily calibration by TCE and/or DCE st

229 Logistic regressions of the raw data reveal that this nonrandom distribution stems p

230 Analysis of Perucca's raw data reveals that he was observing a convolution of

231 cesses automatically algebra combinations of raw data sequencing into a comprehensive final annotated

232 The raw data series are reduced to phase-insensitive summary

233 assist the analyst in visualizing the entire raw data set and as a result, most of the data are not a

234 By using essentially the same raw data set as Collard and Wood, 65 quantitative cranio

235 Each raw data set was reconstructed with bone and soft-tissue

236 mages were formed from the same full-Fourier raw data set.

237 nance (MR) imaging produce large unprocessed raw data sets in minutes.

238 Raw data sets were reconstructed by using filtered back

239 d related components, capable, from the same raw data sets, of enabling increased assay sensitivity a

240 rchiving protocols to minimize bias from the raw data sources.

241 agrams and how to estimate HAT barriers from raw data, starting with the simplest reaction H + H2 and

242 search is needed on the preprocessing of the raw data, such as the normalization step and filtering,

243 Analysis of the study methodology and raw data suggest that this estimate is statistically fla

244 the extraction of relevant information from raw data, the scale of the projects involved and the sta

245 Two effects have been shown to affect the raw data: the sequence dependence of the probe hybridiza

246 atistical data about clinical trials but not raw data; this database may be a model for data from stu

247 d through the entire analysis workflow, from raw data through preprocessing (including a wide range o

248 user-friendly graphics-based package, takes raw data through the entire processing procedure and, im

249 ysis was performed by post-processing of the raw data (time intensity curve [TIC] analysis).

250 Covering the spectrum from raw data to assembled and functionally annotated genomes

251 include data on calibrations and sufficient raw data to assess precision and accuracy of the results

252 , we investigated the use of sampling of the raw data to estimate capillary pressure.

253 r friendly complete pipeline from processing raw data to reporting analytic results; (ii) it detects

254 We combined the available raw data to strengthen the current literature in compari

255 It aims to fully encompass and simplify the "raw data-to-publication" pathway and make it reproducibl

256 ully recover the recurrent CNV patterns from raw data under different scenarios.

257 repositories, the experiment annotation and raw data uploading can be very timeconsuming, especially

258 mprehensive) map, a list of STS primers, and raw data used in map assembly are available at our Web s

259 We processed raw data using a graphical-based algorithm by transformi

260 Processing of raw data using the XCMS software resulted in time-aligne

261 However, while innovative means to share raw data, validate observations, and disseminate scienti

262 and visualize the results as overlays on the raw data via any web browser using a personal computer o

263 play, statistical analysis and access to the raw data via web services.

264 Raw data was only handled by coauthors with direct affil

265 Raw data was preprocessed using SAGE (GE) software.

266 With use of raw data, we recalculated all participant assignments to

267 A) of 2001 provides an avenue for request of raw data, we reviewed all IQA requests to the U.S. EPA i

268 Raw data were analyzed using SAGE (GE) software.

269 The raw data were compiled, descriptive analyses were perfor

270 ures ranging from 5 to 37 degrees C, and the raw data were fit globally to derive a single set of rat

271 Raw data were grouped into nine dimensions of periodonta

272 nalysts and investigators with access to the raw data were masked to study group by coding the groups

273 Raw data were obtained for 698 patients from the 12 iden

274 Raw data were obtained from the authors and quality cont

275 Obtained raw data were processed using multivariate statistical a

276 The four-color raw data were processed, base-called by the sequencing s

277 EXTRACTION: All data were renormalized from raw data, when available, using consistent methods.

278 ethod that is applicable for analysis of the raw data where there are often more than a million rows

279 prises a significant portion of the measured raw data, which can have serious implications for the in

280 complex mixtures, produces large amounts of raw data, which needs to be analyzed to identify molecul

281 iKit assembly is a reduced representation of raw data while retaining most of the original informatio

282 utomatic local thresholding of the resultant raw data with the Phansalkar method was analyzed with ge

283 ating signals and signals extracted from PET raw data with the sensitivity method, by applying princi

284 reliability and noise characteristics of the raw data, with important consequences for the power to d