ライフサイエンスコーパス: data set

1 expression in both injury ages (63% of P7SCI data set).

2 ts model weighted by the sample size of each data set.

3 ied by using billing codes in the 5% Limited Data Set.

4 ve images are added to the existing training data set.

5 lved in a multiple factorial analysis of the data set.

6 s (at rank-1) by 9-23% depending on the used data set.

7 between uveitis and lymphoma in the aqueous data set.

8 uce a "one-size-fits-all" ribosome profiling data set.

9 t was trained and tested on a large clinical data set.

10 h ADHD risk in the discovery and replication data sets.

11 ort was divided into training and validation data sets.

12 hat allows for a rapid preannotation of HRMS data sets.

13 f genetic variants in large-scale sequencing data sets.

14 ate our observations in multiple independent data sets.

15 hat should be reported with any new RIBO-Seq data sets.

16 olomics studies, representing 148 individual data sets.

17 common regulatory patterns across scATAC-seq data sets.

18 thmic plots in representation of calibration data sets.

19 from three cancer gene expression benchmark data sets.

20 ysis software exists to mine these extensive data sets.

21 a lack of large, heterogeneous and granular data sets.

22 gh detection of hidden patterns within large data sets.

23 identification of microplastics in spectral data sets.

24 TMS targets across independent retrospective data sets.

25 split into training and validation and test data sets.

26 signs (P < .001) in 100% cases in all three data sets.

27 s extremely underrepresented in these linked data sets.

28 equire user seeding or well-defined training data sets.

29 hat can clarify relationships within complex data sets.

30 pipelines using both empirical and simulated data sets.

31 nt hitters" using 872 publicly available HTS data sets.

32 r, which ensures availability of methods and data sets.

33 e scale electronic health record and genomic data sets.

34 ms of nanotoxicity across extremely variable data sets.

35 genes from high dimensional gene expression data sets.

36 benefits of this approach using two in vivo data sets.

37 builds transcript models from pooled RNA-seq data sets.

38 ression measurements of bulk and single-cell data sets.

39 arlo simulations and application to specific data sets.

40 ility of these methods to three experimental data sets.

41 eviation]; 3101 women) were evaluated across data sets.

42 higher likelihood of accessible metagenomic data sets.

43 and annotate cells across three human cortex data sets.

44 trajectories across multiple conditions and data sets.

45 enabling factorization of large single-cell data sets.

46 cular targets by meta-analysis of microarray data sets.

47 y expressed in basal-like BC across multiple data sets.

48 ive identification of signals in challenging data sets.

49 increasing availability of complex clinical data sets.

50 rospectively gated axial gradient-echo (GRE) data sets.

51 ation of PRAM to mouse hematopoietic RNA-seq data sets.

52 cogenomics and schizophrenia gene expression data sets.

53 del CAD system using 5 independent endoscopy data sets.

54 5 vs. PLCO(M2012) AUC of 0.751) and the NLST data set (0.659 vs. 0.650).

55 o radiologists blinded to all clinical data; data set 1 contained pre- and postcontrast sequences (co

56 I HF in comparison with event-free controls (data set 1).

57 trast sequences (contrast-enhanced MRI), and data set 2 contained precontrast and DWI sequences (DWI)

58 plasma proteins associated with post-MI HF (data set 2), whereas single-cell transcriptomes identifi

59 ages that contained early-stage neoplasia in data sets 2-5 were delineated in detail for neoplasm pos

60 tomes identified 15 gene-protein candidates (data set 3).

61 detected neoplasia in 97% and 92% of cases (data sets 4 and 5, respectively).

62 In data set 5 (80 patients and images) values for the CAD s

63 Data set 5 was also scored by 53 general endoscopists wi

64 We trained/tested on an expansive data set (6,556 images), and performed an active learnin

65 ld on a multisite approach, assembling large data sets across diverse populations, and will also leve

66 nexplored aspects in plant LD functions, our data set allowed for a comparative analysis of the LD pr

67 d to analyse multiple transposon mutagenesis data sets allowing this technology to be used at large s

68 demonstrate how wider analysis of the entire data set alongside standard analyses of quality control

69 usion of several nearby tissue types in this data set also led to our identification of functional co

70 Furthermore, clinical colorectal cancer data set analysis showed that down-regulation of SMPD1 w

71 , impacting bulk and single cell (scRNA-seq) data set analysis.

72 One training data set and an independent test set were collected from

73 ical data analysis was used to visualize the data set and cluster analysis performed at genus level.

74 formation in a Alzheimer's disease biomarker data set and from a comparison of tissues in Homo sapien

75 For classification, a reference wine data set and mass spectra of different marijuana extract

76 ned for users that only want to upload their data set and select the functions they need calculated f

77 Our data set and the findings from this study could be used

78 ase II and III trials provided the discovery data set and were subdivided into discovery and internal

79 iterature mining), totaling 5019 unique GWAS data sets and 15 770 trait-associated gene sets.

80 methods for Biocrates kits and other target data sets and creates a comprehensive quality control re

81 Therefore, our data sets and models are made freely available.

82 ed using existing single-cell transcriptomic data sets and new data modeling drug-resistance acquisit

83 rmal tissues (n = 23), from public-available data sets and our patient cohort.

84 tive groups, and validation in both existing data sets and prospective studies.

85 e mass spectrometry (MS) and ion mobility-MS data sets and provide a brief overview of currently avai

86 ciated at the level of P < 1e-05 in all GWAS data sets and that SNPs with P-values above 0.2 were inf

87 he authors tested for reproducibility across data sets and whether symptom-specific targets derived f

88 is fast enough to easily handle the largest data sets and, as such, it is a useful tool to add into

89 hallenges to applying these methods to large data sets, and discuss ways forward for the field.

90 In a re-analysis of two prior data sets, and in another experiment, we reveal several

91 ng, integration and visualization of complex data sets; and application - the interpretation and hypo

92 putationally inferred from large metagenomic data sets are often incomplete and may be missing functi

93 Data from multiple phosphoproteomic data sets are provided as web-based resources.

94 Two data sets are used to demonstrate the functionality of t

95 trolled trial cohorts served as the training data set (ARMA [High vs. Low Vt], ALVEOLI [Assessment of

96 method which rapidly deals with these large data sets as well as methods for optimally selecting loc

97 have not directly transferred to metagenomic data sets, as assumptions made by the single genome asse

98 g six additional algorithms for the training data sets, Assay Central performed similarly at a reduce

99 althy people, from each family, composed the data set associated with AgP.

100 zation factors are provided as global raster data sets at high spatial resolution (~1 km) and for lar

101 t enables such integration across scATAC-seq data sets by applying the CoGAPS Matrix Factorization al

102 As such data sets can be large and data analyses laborious, impr

103 Analysis of genomic data sets can provide high-resolution estimates of genet

104 For the macaque data set, coherence and our new MIF estimator largely ag

105 By obtaining a decomposition of data sets collected by the Federal Highway Administratio

106 The two data sets combined showed significant negative dose-resp

107 ormance of CV19-Net, a randomly sampled test data set composed of 500 chest radiographs in 500 patien

108 have produced open-source tools and a public data set consisting of tumor/TIL maps for 1090 invasive

109 nosequencing data sets, suggesting that such data sets contain many more tandem fusions than previous

110 We find that many real-world data sets contain regions with widely heterogeneous dime

111 The data set contained 24,756 direct clinical observations o

112 examined expression of ACE2 and TMPRSS2 in 2 data sets containing gene expression data from nasal and

113 m ChEMBL; subvalidations using this training data set correctly predicted 58% of inhibitors when anal

114 er symptom-specific targets derived from one data set could predict symptom improvement in the other

115 tial resolution, the resulting image-derived data sets could be combined with molecular large-scale d

116 risk score with 11 inputs, in both the PLCO data set (CXR-LC AUC of 0.755 vs. PLCO(M2012) AUC of 0.7

117 This data set defines the dynamic genomic regulatory landscap

118 erse samples from a previously characterized data set derived from DNA extracted from biofilms dislod

119 omputational system for analysis of genomics data sets, designed to accelerate biomedical discovery.

120 Analysis of expressed SNVs in the scRNA-seq data set distinguished recipient versus donor origin for

121 ck hub, enabling researchers to explore this data set easily in a genome browser.

122 can estimate the log-likelihood of an entire data set efficiently and without bias.

123 olutional neural networks jointly on the two data sets enables very high (R(2) > 0.79) predictive acc

124 DENT-seq produces a single deep sequence data set enriched for reads near nick sites and establis

125 c monitoring, resulting in a highly detailed data set for particle numbers, particle shapes, and poly

126 s performance on synthetic data and two real data sets for lipid traits and Alzheimer's disease.

127 Additionally, in our data set, for several regions, male and female volumetri

128 3-dimensional LGE-cardiac magnetic resonance data set from 207 labeled scans.

129 Finally, we analyzed an interesting data set from 396 vaginal microbiome samples where the g

130 n statistical framework and a large sequence data set from bat-CoVs (including 630 novel CoV sequence

131 A data set from China Health and Retirement Longitudinal S

132 in vitro model (CS vs. air) with a published data set from human epithelial brushes (smoker vs. non-s

133 were further investigated in an independent data set from The Cancer Genomic Atlas, and demonstrated

134 rt (TDT) and prognosis in a large real-world data set from the German Study Alliance Leukemia-Acute M

135 rk has been applied to the real experimental data sets from a neutron grating interferometer and we h

136 u(ef)) using large-scale top-down proteomics data sets from CZE-MS/MS.

137 are coexpressed with TSAR3 in transcriptome data sets from developing M. truncatula seeds led to the

138 ce significantly drops if applied to testing data sets from different European populations.

139 using frozen tissue samples, including many data sets from our own group, were often collected and a

140 We created 3 data sets from patients with at least one AF billing cod

141 sly published comparative functional genomic data sets from primates using frozen tissue samples, inc

142 our approach to identify regulatory genes in data sets from single cell gene expression and from abio

143 The current version of TSEA-DB includes 4423 data sets from the UK Biobank (UKBB) and 596 from other

144 mportant mites based on total RNA sequencing data sets generated in this study as well as those depos

145 Using multiple data sets generated with different experimental protocol

146 n order to realize the full potential of the data sets generated.

147 orporates the assignments into the processed data set, generating a series of interactive plots, EICs

148 of five data types that overlap with outside data sets: geographic location (9 studies), medical data

149 ers were assessed in Gene Expression Omnibus data sets (GSE30063, GSE108134, and GSE11784).

150 int analysis of large collections of RNA-seq data sets has emerged as one such analysis.

151 The number of spatiotemporal data sets has increased rapidly in the last years, which

152 Thousands of epigenomic data sets have been generated in the past decade, but it

153 The results and data set here could provide insights into oxidative stre

154 parative informatics between human and mouse data sets identified shared EN subtype markers, which we

155 Applying PRAM to 30 human ENCODE RNA-seq data sets identified unannotated transcripts with epigen

156 been deposited to ProteomeXchange, with the data set identifier PXD019597.

157 These new data sets improve convergence and expand the high-confid

158 cing data, which is a more prevalent type of data set in metagenomics studies.

159 no standardized protocol for analyzing these data sets in a reproducible manner.

160 ed across up to sixteen multiple association data sets in a single view using the integrated genome b

161 ses a new challenge for analysis of multiple data sets in conjunction.

162 observation of independent yet inconsistent data sets in the literature.

163 ss tens of thousands of bulk and single-cell data sets in the public archive.

164 e issues on the basis of currently available data sets in this rapidly moving field.

165 Each data set included measures of serum ferritin (SF), vitam

166 enriched proteins that were common to all 3 data sets included well-established biomarkers of post-M

167 lly available maize (Zea mays) transcriptome data sets including >6000 RNA sequencing samples to gene

168 tients requires the integration of different data sets, including genomic profiles, tumour histopatho

169 ion estimation is also demonstrated on other data sets, including human fetal echocardiography, chick

170 ugh removal of the need to manually divide a data set into many time windows and analyze each one, a

171 tivariate analysis was able to decompose the data set into the different proteins, but the multicompo

172 were employed in the analysis of an archived data set involving polyarthritic subjects, the number of

173 The data set is at the health center grantee level and does

174 iols, an internally consistent thermodynamic data set is created, which we recommend to be used in st

175 diovascular patients once this comprehensive data set is subjected to unique, integrative analytical

176 Interpretation of large and complicated data sets is a significant challenge, for which multiple

177 Finally, the augmentation of limited data sets is demonstrated in a method informed by unsupe

178 neralization of the signature to similar CRC data sets is predicted to be high.

179 ncreasing the spatiotemporal coverage of OMP data sets is through the active involvement of citizen v

180 Next, we analyze a comprehensive new data set measuring the transcriptional response shortly

181 Particularly, for large-size data sets (more than 40,000 cells), SHARP runs faster th

182 In our data set, most SIVH were male (Iraqi: 59.7%; Afghan: 54.

183 al rate determination for processing kinetic data sets, no simple and automated program existed for r

184 We also present a new yeast binding location data set obtained by transposon calling cards and compar

185 is then validated over thousands of pairs of data sets obtained by random partitions of large studies

186 hus, the novel genetically modified rats and data sets obtained in this study will advance our knowle

187 We used a prototype data set of 107 subjects which are comprised of 38 non-p

188 of 1,746 chemicals compiled from a combined data set of 11 ToxCast(TM)/Tox21 HTS in vitro assays.

189 This paper utilized an imbalanced data set of 24,434 with (69.71%) non-hypertensive patien

190 We built a comprehensive data set of 30 MeONPs to screen a proinflammatory cytoki

191 A large data set of 30,000 virtual, porous microstructures of di

192 and integrated these data with an orthogonal data set of 352 nonredundant, in vitro-derived motifs ma

193 FEP predictions of protein stability over a data set of 87 mutations on five different proteins has

194 images (200 x) were acquired (15.61 TB).The data set of block-face images (96.2 GB) was also capture

195 acterization factors are applied to a global data set of coal power emissions.

196 alysis, based on the most complete available data set of mortality events from PTXD randomized contro

197 In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluoro

198 metabolites from a single NMR sample, and a data set of one-dimensional (1D) (31)P NMR and 2D (1)H-(

199 g a general reactivity probe, we generated a data set of proteomic cysteine residues sensitive to the

200 By obtaining a complete data set of PSMalpha3 peptides in solution and with n-do

201 Here, we used a large data set of published and newly generated sRNA sequencin

202 Here we introduce a new data set of RNA elements in the human genome that are re

203 ics and to generate the largest experimental data set of selective effect concentrations of antibioti

204 cular representation is learned from a large data set of structures with m/z labels.

205 Here we use a large data set of water stable isotope ratios (n = 1150) to sh

206 Systematic data sets of both types exist for yeast and human, but t

207 nsically high dimensional and generate noisy data sets of ever-increasing size.

208 om reanalyses of multiple publicly available data sets of human and mouse heart failure, demonstrated

209 for new tools to generate and analyze large data sets of mitochondrial images in high throughput.

210 Gene expression data sets of Multiple tissues and Yeast from two differe

211 sponding pathology results from two external data sets of patients with HNSCC: an external institutio

212 e statistical model, we examined the longest data set on floating plastic debris available globally,

213 Results are reported for validation data sets only.

214 We provide a detailed analysis of this data set organized on the basis of structure and functio

215 When applied to this preliminary DR data set, our density-based method demonstrated better p

216 both local and global interpretations of our data sets, preserving the chemical heterogeneity uncover

217 ipal component analysis (cPCA) on an RNA-Seq data set profiling gene expression of the external granu

218 Our ChIP-seq and RNA-seq data sets provide an excellent resource for comprehensiv

219 e and is subsequently applied to the one-off data set recorded by the spatially extensive network of

220 mance of ConSReg, we analyzed an independent data set related to plant nitrogen responses.

221 6 for training, testing, validation, and all data sets, respectively, which shows good agreement betw

222 These data sets reveal dynamic changes of core liver functions

223 The GTEx data set revealed regulatory functions of rs6008845 on P

224 tudy, in silico analysis of TCGA lung cancer data sets revealed a significant increase in LYCAT expre

225 The real-time data sets revealed extraction kinetics for VOCs present

226 Both data sets roughly partitioned wetland numbers equally be

227 ,022), and a fourth served as the validation data set (SAILS [Statins for Acutely Injured Lungs from

228 All data sets showed accelerations in SZ for the 3 mortality

229 All three data sets showed evidence of extranuclear RNA contaminat

230 Validation on real and simulated data sets shows that MPL is fast and accurate, outperfor

231 and signal-to-noise ratios across different data sets simultaneously by a monotonic nonlinear transf

232 Further analysis using a "bounding" data set spanning a range of realistic water Pb concentr

233 Here we analyse data sets spanning three decades, to investigate whether

234 sions in antigen-stimulated immunosequencing data sets, suggesting that such data sets contain many m

235 train and evaluate ThermoNet with a curated data set that accounts for protein homology and is balan

236 Our results provide a multihazard data set that can be leveraged for epidemiological resea

237 We illustrate our workflow on a large data set that contains bacterial species related to urin

238 algorithm in conjunction with a CD reference data set that contains soluble and denatured proteins (S

239 s were raised regarding coding errors in the data set that formed the basis of this study.

240 proceeded, and was completely missing in the data set that had been generated using cells selected on

241 tein structures using a previously published data set that identified evolutionarily conserved rare c

242 d the spread and control of COVID-19 using a data set that included case reports, human movement, and

243 d, and evaluated the availability of outside data sets that overlap with study data.

244 Today, there is a strong push toward sharing data sets through public repositories in many research f

245 The Wolffia genome and TOD expression data set thus provide insight into the interplay between

246 This reduces the entire hyperspectral data set to a single reconstructed color similarity map,

247 We have compiled the most comprehensive data set to date of long-term (1970-2009) summertime ver

248 etermined by comparing the scores of the DWI data set to those of the clinical reference standard.

249 We constructed a reference data set to train our classification method, which inclu

250 could be combined with molecular large-scale data sets to enable unprecedented systems-level computat

251 This analysis highlights the power of large data sets to identify the diversity of MPS cellular phen

252 pression patterns were compared with curated data sets to identify upstream regulators.

253 ritten in C++ designed to convert structural data sets to realistic geometric meshes while preserving

254 nervous system and leverage ENCODE and GTEx data sets to study the unique splicing of photoreceptors

255 e fit a multidimensional function to a given data set using an auxiliary nonadditive approach.

256 nctional analysis of relevant transcriptomic data sets using a common approach, independent from the

257 l parameters were simultaneously fit to many data sets using a variety of succinate oxidation and fre

258 alysis has been first performed on generated data sets using double bond equivalents (DBE) versus num

259 ousands of citizens generate spatially dense data sets using low-cost passive samplers for nitrogen d

260 The data set was merged with claims data for patients in acc

261 ry classification of higher-density catheter data set was significantly higher than that of lower-den

262 Our open-source data set was typically consistent with data from other s

263 In the validation data set, we also compared our models with another model

264 Therefore, for 1951-2018 in a corresponding data set, we determined changes as linear trends and ana

265 e Cancer Genome Atlas acute myeloid leukemia data set, we found an inverse correlation of miR-146a le

266 rlapping the T1D and T2D groups with the BFA data set, we identified 120 and 204 differentially expre

267 Using a publicly-available data set, we show that BRAID more accurately captures va

268 me or phage-derived sequences in metagenomic data sets, we are unable to assign a function to 50-90%

269 Using surveys of Gene Expression Omnibus data sets, we confirm that fasting suppresses liver adro

270 umor gene expression data from 4 independent data sets, we correlated gene expression with recurrence

271 Using multiple synthetic and real data sets, we demonstrate ACTIONet's superior performanc

272 d surface EEG recordings in four independent data sets, we demonstrate that the 1/f spectral slope of

273 discovery from large collections of RNA-seq data sets, we developed a novel '1-Step' approach named

274 TF-target relationships in natural variation data sets, we found that presence/absence changes rather

275 Ribo-seq footprint data and three benchmark data sets, we illustrate that RiboDiPA can uncover meani

276 Using a number of real ChIP-seq data sets, we observed that MAnorm2 clearly outperformed

277 By integrating both data sets, we reveal that most members of the nine-membe

278 Using artificial and real data sets, we show how chronnets can capture data proper

279 ng simulated and experimental RNA-sequencing data sets, we show that GSECA provides higher performanc

280 ies and analysis of public and in-house real data sets, we successfully demonstrated the validity and

281 oducibility of StatDns across replicate Hi-C data sets, we use this implied StatDn - kNN relationship

282 Further, the genes common to at least two data sets were analyzed using DisGeNET, which showed the

283 To exemplify the concepts and tools, two data sets were analyzed; 1) a set that included artifici

284 Phylogenetic trees based on different data sets were consistent with one another, with the IR,

285 soforms expressed in cardiac tissue, various data sets were fitted simultaneously using global optimi

286 Independent CNN training and testing data sets were maintained with a 4:1 ratio.

287 r >3 years, complete day-to-day heart rhythm data sets were reconstructed for every participant, incl

288 Two data sets were scored by two radiologists blinded to all

289 Replication in the 23andMe data set, where RD is self-reported by participants, fir

290 In the UK Biobank data set, where RD was ascertained by self-report or hos

291 ce in their corresponding population testing data sets, whereas their performance significantly drops

292 y, were trained using the large experimental data set, which enabled the generation of a large predic

293 affected between standard and HRdm processed data sets, which allowed statistically identical collisi

294 We created an open-source data set with data at the county level on exposure to fo

295 Intersection of our data set with genes on cardiac clinical testing panels a

296 h logarithm of concentration, fitting such a data set with linear function, and deriving method chara

297 A data set with the 2015 to 2017 nutritional information w

298 ltiple simulated and real biological RNA-seq data sets with positive control outlier samples.

299 Large data sets within clinical microbiology that are amenable

300 enabling success with progressively smaller data sets without overfitting.